Amruta Moktali

Generative pre-trained transformer models, or GPT models, have countless applications and are being rapidly deployed across a wide range of domains. However, using GPT models without appropriate safeguards can lead to leakage of sensitive data. This concern underscores the critical need for privacy and data protection. Skyflow LLM Privacy Vault prevents sensitive data from reaching GPTs. Amruta Moktali is the Chief Product Officer at Skyflow and she joins us today. We discuss generative AI, how the technology is different from other AI approaches, and how we can use this technology in a safe and ethical manner. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from information visualization to quantum computing. Currently, Sean is Head of Marketing and Developer Relations at Skyflow and host of the podcast Partially Redacted, a podcast about privacy and security engineering. You can connect with Sean on Twitter @seanfalconer . Sponsorship inquiries: sponsor@softwareengineeringdaily.com Please click here to view this show’s transcript. The post Ethical GPTs with Amruta Moktali appeared first on Software Engineering Daily.

Generative pre-trained transformer models, or GPT models, have countless applications and are being rapidly deployed across a wide range of domains. However, using GPT models without appropriate safeguards can lead to leakage of sensitive data. This concern underscores the critical need for privacy and data protection. <a href="https://www.skyflow.com/post/generative-ai-data-privacy-skyflow-llm-privacy-vault" rel="nofollow" target="_blank">Skyflow LLM Privacy Vault</a> prevents sensitive data from reaching GPTs. <a href="https://www.linkedin.com/in/amrutamoktali/" rel="nofollow" target="_blank">Amruta Moktali</a> is the Chief Product Officer at Skyflow and she joins us today. We discuss generative AI, how the technology is different from other AI approaches, and how we can use this technology in a safe and ethical manner. <a href="https://i0.wp.com/softwareengineeringdaily.com/wp-content/uploads/2022/05/Sean-Falconer.png" rel="nofollow" target="_blank"></a>Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from information visualization to quantum computing. Currently, Sean is Head of Marketing and Developer Relations at <a href="https://www.skyflow.com/" rel="nofollow" target="_blank">Skyflow</a> and host of the podcast Partially Redacted, a podcast about privacy and security engineering. You can connect with Sean on Twitter <a href="https://twitter.com/seanfalconer" rel="nofollow" target="_blank">@seanfalconer .</a> Sponsorship inquiries: <a href="https://cms.megaphone.fm/organizations/619b680e-d813-11ea-9750-e7ecac406436/podcasts/e60108fe-e328-11ea-b2b6-1348249f900a/episodes/3913370a-bc44-11ed-ace6-8b1f96d82bba/sponsor@softwareengineeringdaily.com" rel="noopener noreferrer nofollow" target="_blank">sponsor@softwareengineeringdaily.com</a> <a href="http://softwareengineeringdaily.com/wp-content/uploads/2023/07/SED1548-Amruta-Moktali.txt" rel="nofollow" target="_blank">Please click here to view this show’s transcript</a>. The post <a href="https://softwareengineeringdaily.com/2023/07/27/ethical-gpts-with-amruta-moktali/" rel="nofollow" target="_blank">Ethical GPTs with Amruta Moktali</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

Ethical GPTs with Amruta Moktali

Matt Turck

There are many types of early stage funding available from friends and family to seed to series A.  Some firms invest across a wide set of technologies and seek only to provide capital. Others are in it for the long haul – they focus on specific areas of technology and develop both long term relationships and deep expertise over time.   Today, we are interviewing Matt Turck of First Mark Capital, who is in it for the long haul and whose portfolio companies include Dataiku, Crossbeam, Ada, Cockroach Labs, Clickhouse and more.  Today we will talk about Matt’s career, investment point of view, founding the Data-driven NYC community and the recent release of the 20234 MAD  – an industry resource for understanding the Machine Learning, AI and Data Landscape  Be sure to check out the show notes for links to the MAD This epsiode is hosted by Jocelyn Houle. Follow Jocelyn on Linked or on Twitter @jocelynbyrne.   Show notes –  In today’s show we referenced a couple things you may want to check out. Matt’s blog and MAD Landscape The interactive MAD Landscape The picture in Matt’s Office was The Son of Man by Rene Magritte  Matt’s full bio FirstMark Capital Site Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Data Investing and the MAD with Matt Turck appeared first on Software Engineering Daily.

<a href="https://i0.wp.com/softwareengineeringdaily.com/wp-content/uploads/2023/03/matt.png" rel="nofollow" target="_blank"></a>There are many types of early stage funding available from friends and family to seed to series A.  Some firms invest across a wide set of technologies and seek only to provide capital. Others are in it for the long haul – they focus on specific areas of technology and develop both long term relationships and deep expertise over time.   Today, we are interviewing <a href="https://twitter.com/mattturck?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor" rel="nofollow" target="_blank">Matt Turck</a> of <a href="https://firstmarkcap.com/" rel="nofollow" target="_blank">First Mark Capital</a>, who is in it for the long haul and whose portfolio companies include Dataiku, Crossbeam, Ada, Cockroach Labs, Clickhouse and more.  Today we will talk about Matt’s career, investment point of view, founding the Data-driven NYC community and the recent release of the 20234 MAD  – an industry resource for understanding the Machine Learning, AI and Data Landscape Be sure to check out the show notes for links to the MAD This epsiode is hosted by Jocelyn Houle. Follow Jocelyn on Linked or on Twitter <a href="https://twitter.com/jocelynbyrne" rel="nofollow" target="_blank">@jocelynbyrne</a>. Show notes –  In today’s show we referenced a couple things you may want to check out. Matt’s <a href="https://mattturck.com/mad2023/#more-1693" rel="nofollow" target="_blank">blog and MAD Landscape</a> The <a href="https://mad.firstmarkcap.com/" rel="nofollow" target="_blank">interactive MAD Landscape</a> The picture in Matt’s Office was <a href="https://en.wikipedia.org/wiki/The_Son_of_Man" rel="nofollow" target="_blank">The Son of Man by Rene Magritte</a> <a href="https://firstmarkcap.com/team/matt-turck/" rel="nofollow" target="_blank">Matt’s full bio</a> <a href="https://firstmarkcap.com/" rel="nofollow" target="_blank">FirstMark Capital Site</a> Sponsorship inquiries: <a href="https://cms.megaphone.fm/organizations/619b680e-d813-11ea-9750-e7ecac406436/podcasts/e60108fe-e328-11ea-b2b6-1348249f900a/episodes/3913370a-bc44-11ed-ace6-8b1f96d82bba/sponsor@softwareengineeringdaily.com" rel="noopener noreferrer nofollow" target="_blank">sponsor@softwareengineeringdaily.com</a> The post <a href="https://softwareengineeringdaily.com/2023/03/10/data-investing-and-the-mad/" rel="nofollow" target="_blank">Data Investing and the MAD with Matt Turck</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

Data Investing and the MAD with Matt Turck

Christian Hubicki

ChatGPT is an artificial intelligence language model developed by OpenAI. It is part of the GPT (Generative Pre-trained Transformer) family of models, which are designed to generate human-like text based on input prompts. ChatGPT is specifically trained to carry out conversational tasks, such as answering questions, completing sentences, and engaging in dialogue. It has been pre-trained on a large corpus of text data and fine-tuned on specific tasks to improve its performance. As a result, ChatGPT can generate responses that are often coherent, relevant, and natural-sounding. Christian Hubicki is an Assistant Professor in the Robotics Department at Florida State University. He joins us today to discuss ChatGPT and its implications. We also discussed the future of Artificial Intelligence in general. This show is hosted by Sean Falconer. Sean is the Head of Developer Relations and Marketing @Skyflow. Follow Sean at @seanfalconer     Sponsorship inquiries: sponsor@softwareengineeringdaily.com   The post Surviving ChatGPT with Christian Hubicki appeared first on Software Engineering Daily.

<a href="https://i0.wp.com/softwareengineeringdaily.com/wp-content/uploads/2023/02/Rectangle-167.png" rel="nofollow" target="_blank"></a>ChatGPT is an artificial intelligence language model developed by OpenAI. It is part of the GPT (Generative Pre-trained Transformer) family of models, which are designed to generate human-like text based on input prompts. ChatGPT is specifically trained to carry out conversational tasks, such as answering questions, completing sentences, and engaging in dialogue. It has been pre-trained on a large corpus of text data and fine-tuned on specific tasks to improve its performance. As a result, ChatGPT can generate responses that are often coherent, relevant, and natural-sounding. <a href="https://twitter.com/chubicki" rel="nofollow" target="_blank">Christian Hubicki</a> is an Assistant Professor in the Robotics Department at Florida State University. He joins us today to discuss ChatGPT and its implications. We also discussed the <a href="https://i0.wp.com/softwareengineeringdaily.com/wp-content/uploads/2022/05/Sean-Falconer.png" rel="nofollow" target="_blank"></a>future of Artificial Intelligence in general. This show is hosted by <a href="mailto:sean.falconer@skyflow.com">Sean Falconer</a>. Sean is the Head of Developer Relations and Marketing <a href="https://twitter.com/SkyflowAPI" rel="nofollow" target="_blank">@Skyflow</a>. Follow Sean at <a href="https://twitter.com/seanfalconer" rel="nofollow" target="_blank">@seanfalconer</a> Sponsorship inquiries: <a href="sponsor@softwareengineeringdaily.com" rel="nofollow" target="_blank">sponsor@softwareengineeringdaily.com</a> The post <a href="https://softwareengineeringdaily.com/2023/02/24/surviving-chatgpt/" rel="nofollow" target="_blank">Surviving ChatGPT with Christian Hubicki</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

Surviving ChatGPT with Christian Hubicki

Daniel Situnayake

Today, we spoke with Daniel Situnayake of Edge Impulse. We discussed AI, machine learning, edge devices, TinyML and AI tool chain.             Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Edge Impulse with Daniel Situnayake appeared first on Software Engineering Daily.

<a href="https://i0.wp.com/softwareengineeringdaily.com/wp-content/uploads/2022/09/Daniel-Situnayake.png" rel="nofollow" target="_blank"></a>Today, we spoke with <a href="https://twitter.com/dansitu?lang=en" rel="nofollow" target="_blank">Daniel Situnayake</a> of <a href="https://edgeimpulse.com/" rel="nofollow" target="_blank">Edge Impulse</a>. We discussed AI, machine learning, edge devices, TinyML and AI tool chain. Sponsorship inquiries: <a href="mailto:sponsor@softwareengineeringdaily.com" rel="noopener noreferrer">sponsor@softwareengineeringdaily.com</a> The post <a href="https://softwareengineeringdaily.com/2022/09/30/edge-impulse-with-daniel-situnayake/" rel="nofollow" target="_blank">Edge Impulse with Daniel Situnayake</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

Edge Impulse with Daniel Situnayake

Andy Pavlo

The default configuration in most databases is meant for broad compatibility rather than performance. Database tuning is a process in which the configurations of a database are modified to achieve optimal performance. Databases have hundreds of configuration knobs that control various factors, such as the amount of memory to use for caches or how often the data is written to the storage. The problem with these knobs is that  they are not standardized (i.e., two databases may have a different name for the same knob),  not independent (i.e., changing one knob can impact others),  and not universal (i.e., what works for one application may be suboptimal for another).   In reality, information about the effects of the knobs typically comes only from (expensive) experience.  OtterTune is automatic database tuning software that promises to overcome these problems. It uses machine learning to tune the configuration knobs of your database automatically to improve performance. In this episode, we interview Andy Pavlo. Andy is a Database Professor at Carnegie Mellon and Co-Founder of OtterTune. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Automatic Database Tuning with Andy Pavlo appeared first on Software Engineering Daily.

<a href="https://i0.wp.com/softwareengineeringdaily.com/wp-content/uploads/2022/09/Andy-Pavlo.png" rel="nofollow" target="_blank"></a>The default configuration in most databases is meant for broad compatibility rather than performance. Database tuning is a process in which the configurations of a database are modified to achieve optimal performance. Databases have hundreds of configuration knobs that control various factors, such as the amount of memory to use for caches or how often the data is written to the storage. The problem with these knobs is that <ul> <li>they are not standardized (i.e., two databases may have a different name for the same knob),</li> <li> not independent (i.e., changing one knob can impact others), </li> <li>and not universal (i.e., what works for one application may be suboptimal for another). </li> </ul> In reality, information about the effects of the knobs typically comes only from (expensive) experience. <a href="https://ottertune.com/" rel="nofollow" target="_blank">OtterTune</a> is automatic database tuning software that promises to overcome these problems. It uses machine learning to tune the configuration knobs of your database automatically to improve performance. In this episode, we interview<a href="https://twitter.com/andy_pavlo?lang=en" rel="nofollow" target="_blank"> Andy Pavlo</a>. Andy is a Database Professor at Carnegie Mellon and Co-Founder of OtterTune. Sponsorship inquiries: <a href="mailto:sponsor@softwareengineeringdaily.com" rel="noopener noreferrer">sponsor@softwareengineeringdaily.com</a> The post <a href="https://softwareengineeringdaily.com/2022/09/22/automatic-database-tuning/" rel="nofollow" target="_blank">Automatic Database Tuning with Andy Pavlo</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

Automatic Database Tuning with Andy Pavlo

Charlie Gerard

Originally published on January 1, 2022. Charlie Gerard is an incredibly productive developer.  In addition to being the author of Practical Machine Learning in JavaScript, her website charliegerard.dev has a long list of really interesting side projects exploring the intersection of human computer interaction, computer vision, interactivity, and art.  In this episode we touch on some of these projects and broadly explore how practical it is to bring interesting HCI concepts into one’s work. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Practical Machine Learning in JavaScript with Charlie Gerard appeared first on Software Engineering Daily.

Originally published on January 1, 2022. <a href="https://twitter.com/devdevcharlie?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor" rel="nofollow" target="_blank">Charlie Gerard</a> is an incredibly productive developer.  In addition to being the author of Practical Machine Learning in JavaScript, her website <a href="https://charliegerard.dev/" rel="nofollow" target="_blank">charliegerard.dev</a> has a long list of really interesting side projects exploring the intersection of human computer interaction, computer vision, interactivity, and art.  In this episode we touch on some of these projects and broadly explore how practical it is to bring interesting HCI concepts into one’s work. Sponsorship inquiries: <a href="mailto:sponsor@softwareengineeringdaily.com" rel="noopener noreferrer">sponsor@softwareengineeringdaily.com</a> The post <a href="https://softwareengineeringdaily.com/2022/09/18/practical_machinelearning_in-javascript/" rel="nofollow" target="_blank">Practical Machine Learning in JavaScript with Charlie Gerard</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

Practical Machine Learning in JavaScript with Charlie Gerard

Ketan Umare

 At Lyft, Ketan Umare worked on Flyte, an orchestration system for machine learning. Flyte provides reliability and APIs for machine learning workflows, and is used at companies outside of Lyft such as Spotify. Since leaving Lyft, Ketan founded Union.ai, a company focused on productionizing Flyte as a service. He joins the show to talk about the architecture and usage of Flyte, as well as how he is formulating the company around it. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Union.ai with Ketan Umare appeared first on Software Engineering Daily.

At Lyft, Ketan Umare worked on Flyte, an orchestration system for machine learning. Flyte provides reliability and APIs for machine learning workflows, and is used at companies outside of Lyft such as Spotify. Since leaving Lyft, Ketan founded <a href="http://Union.ai" rel="nofollow" target="_blank">Union.ai</a>, a company focused on productionizing Flyte as a service. He joins the show to talk about the architecture and usage of Flyte, as well as how he is formulating the company around it. Sponsorship inquiries: <a href="mailto:sponsor@softwareengineeringdaily.com" rel="noopener noreferrer">sponsor@softwareengineeringdaily.com</a> The post <a href="https://softwareengineeringdaily.com/2022/03/23/union-ai-with-ketan-umare/" rel="nofollow" target="_blank">Union.ai with Ketan Umare</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

Union.ai with Ketan Umare

Darin Fisher

Historically, search engines made money by showing sponsored ads alongside organic results. As the idiom goes, if you’re not paying for something, you are the product. Neeva is a new take on search engines. When you search at neeva.com, you get the type of result you’d expect from a search engine minus any advertising. In this episode, I speak with Darin Fisher, Software Engineer at Neeva. We discuss the motivation, implementation, and mobile experience for searching with Neeva. Sponsorship inquiries: sponsor@softwareengineeringdaily.com   The post Ad-free Search on Neeva with Darin Fisher appeared first on Software Engineering Daily.

Historically, search engines made money by showing sponsored ads alongside organic results. As the idiom goes, if you’re not paying for something, you are the product. Neeva is a new take on search engines. When you search at <a href="http://neeva.com" rel="nofollow" target="_blank">neeva.com</a>, you get the type of result you’d expect from a search engine minus any advertising. In this episode, I speak with Darin Fisher, Software Engineer at Neeva. We discuss the motivation, implementation, and mobile experience for searching with Neeva. Sponsorship inquiries: <a href="mailto:sponsor@softwareengineeringdaily.com" rel="noopener noreferrer">sponsor@softwareengineeringdaily.com</a> The post <a href="https://softwareengineeringdaily.com/2022/01/25/neeva-with-darin/" rel="nofollow" target="_blank">Ad-free Search on Neeva with Darin Fisher</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

Ad-free Search on Neeva with Darin Fisher

Charlie Gerard is an incredibly productive developer.  In addition to being the author of Practical Machine Learning in JavaScript, her website charliegerard.dev has a long list of really interesting side projects exploring the intersection of human computer interaction, computer vision, interactivity, and art.  In this episode we touch on some of these projects and broadly explore how practical it is to bring interesting HCI concepts into one’s work. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Practical Machine Learning in JavaScript with Charlie Gerard appeared first on Software Engineering Daily.

Charlie Gerard is an incredibly productive developer.  In addition to being the author of Practical Machine Learning in JavaScript, her website charliegerard.dev has a long list of really interesting side projects exploring the intersection of human computer interaction, computer vision, interactivity, and art.  In this episode we touch on some of these projects and broadly explore how practical it is to bring interesting HCI concepts into one’s work. Sponsorship inquiries: <a href="mailto:sponsor@softwareengineeringdaily.com" rel="noopener noreferrer">sponsor@softwareengineeringdaily.com</a> The post <a href="https://softwareengineeringdaily.com/2022/01/04/practical-ml-with-charlie-gerard/" rel="nofollow" target="_blank">Practical Machine Learning in JavaScript with Charlie Gerard</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

Once a machine learning model is trained and validated, it often feels like a major milestone has been achieved.  In reality, it’s more like the first lap in a relay race.  Deploying ML to production bears many similarities to a typical software release process, but brings several novel challenges like failing to generalize as expected or model drift. AI Quality management is the biggest challenge in AI today.  In this episode, I interview Anupam Datta, the co-founder at TruEra.  TruEra has a solution aimed at helping with AI performance, monitoring, and model explainability.  We talk about some of the challenges of modern machine learning deployment in production and how companies are succeeding with ML Ops. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Responsibly Deploy AI in Production with Anupam Datta appeared first on Software Engineering Daily.

Once a machine learning model is trained and validated, it often feels like a major milestone has been achieved.  In reality, it’s more like the first lap in a relay race.  Deploying ML to production bears many similarities to a typical software release process, but brings several novel challenges like failing to generalize as expected or model drift. AI Quality management is the biggest challenge in AI today.  In this episode, I interview Anupam Datta, the co-founder at TruEra.  TruEra has a solution aimed at helping with AI performance, monitoring, and model explainability.  We talk about some of the challenges of modern machine learning deployment in production and how companies are succeeding with ML Ops. Sponsorship inquiries: <a href="mailto:sponsor@softwareengineeringdaily.com" rel="noopener noreferrer">sponsor@softwareengineeringdaily.com</a> The post <a href="https://softwareengineeringdaily.com/2021/11/30/ai-in-production-with-anupam/" rel="nofollow" target="_blank">Responsibly Deploy AI in Production with Anupam Datta</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

Responsibly Deploy AI in Production with Anupam Datta

Machine learning and data science episodes of Software Engineering Daily.

Machine Learning Archives - Software Engineering Daily

Rajat Monga

TensorFlow is Google’s open source machine learning library. Rajat Monga is the engineering director for TensorFlow. In this episode, we cover how to use TensorFlow, including an example of how to build a machine learning model to identify whether a picture contains a cat or not. TensorFlow was built with the mission of simplifying the process of deploying a machine learning model from research to production, so we also talk about that, as well as how TensorFlow can be used effectively in combination with Google’s open-source cluster manager, Kubernetes.  The post TensorFlow in Practice with Rajat Monga appeared first on Software Engineering Daily.

TensorFlow is Google’s open source machine learning library. Rajat Monga is the engineering director for TensorFlow. In this episode, we cover how to use TensorFlow, including an example of how to build a machine learning model to identify whether a picture contains a cat or not. TensorFlow was built with the mission of simplifying the process of deploying a machine learning model from research to production, so we also talk about that, as well as how TensorFlow can be used effectively in combination with Google’s open-source cluster manager, Kubernetes. The post <a href="https://softwareengineeringdaily.com/2016/08/18/tensorflow-in-practice-with-rajat-monga/" rel="nofollow" target="_blank">TensorFlow in Practice with Rajat Monga</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

TensorFlow in Practice with Rajat Monga

Rajat Monga is a director of engineering at Google where he works on TensorFlow. TensorFlow is a framework for numerical computation developed at Google.  The majority of TensorFlow users are building machine learning applications such as image recognition, recommendation systems, and natural language processing–but TensorFlow is actually applicable to a broader range of scientific computation than just machine learning. TensorFlow has APIs for decision trees, support vector machines, and linear algebra libraries. The current focus of the TensorFlow team is usability. There are thousands of engineers building data-intensive applications with TensorFlow, but Rajat and the rest of the TensorFlow team would like to see millions more. In today’s show, Rajat and I discussed how TensorFlow is becoming more usable, as well as some of the developments in TensorFlow around edge computing, TensorFlow Hub, and TensorFlow.js, which allows TensorFlow to run in the browser. The post TensorFlow Applications with Rajat Monga appeared first on Software Engineering Daily.

Rajat Monga is a director of engineering at Google where he works on TensorFlow. TensorFlow is a framework for numerical computation developed at Google. The majority of TensorFlow users are building machine learning applications such as image recognition, recommendation systems, and natural language processing–but TensorFlow is actually applicable to a broader range of scientific computation than just machine learning. TensorFlow has APIs for decision trees, support vector machines, and linear algebra libraries. The current focus of the TensorFlow team is usability. There are thousands of engineers building data-intensive applications with TensorFlow, but Rajat and the rest of the TensorFlow team would like to see millions more. In today’s show, Rajat and I discussed how TensorFlow is becoming more usable, as well as some of the developments in TensorFlow around edge computing, TensorFlow Hub, and TensorFlow.js, which allows TensorFlow to run in the browser. The post <a href="https://softwareengineeringdaily.com/2018/04/26/tensorflow-applications-with-rajat-monga/" rel="nofollow" target="_blank">TensorFlow Applications with Rajat Monga</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

TensorFlow Applications with Rajat Monga

David Aronchick

When TensorFlow came out of Google, the machine learning community converged around it. TensorFlow is a framework for building machine learning models, but the lifecycle of a machine learning model has a scope that is bigger than just creating a model. Machine learning developers also need to have a testing and deployment process for continuous delivery of models. The continuous delivery process for machine learning models is like the continuous delivery process for microservices, but can be more complicated. A developer testing a model on their local machine is working with a smaller data set than what they will have access to when it is deployed. A machine learning engineer needs to be conscious of versioning and auditability. Kubeflow is a machine learning toolkit for Kubernetes based on Google’s internal machine learning pipelines. Google open sourced Kubernetes and TensorFlow, and the projects have users AWS and Microsoft. David Aronchick is the head of open source machine learning strategy at Microsoft, and he joins the show to talk about the problems that Kubeflow solves for developers, and the evolving strategies for cloud providers. David was previously on the show when he worked at Google, and in this episode he provides some useful discussion about how open source software presents a great opportunity for the cloud providers to collaborate with each other in a positive sum relationship. The post Kubeflow: TensorFlow on Kubernetes with David Aronchick appeared first on Software Engineering Daily.

When TensorFlow came out of Google, the machine learning community converged around it. TensorFlow is a framework for building machine learning models, but the lifecycle of a machine learning model has a scope that is bigger than just creating a model. Machine learning developers also need to have a testing and deployment process for continuous delivery of models. The continuous delivery process for machine learning models is like the continuous delivery process for microservices, but can be more complicated. A developer testing a model on their local machine is working with a smaller data set than what they will have access to when it is deployed. A machine learning engineer needs to be conscious of versioning and auditability. Kubeflow is a machine learning toolkit for Kubernetes based on Google’s internal machine learning pipelines. Google open sourced Kubernetes and TensorFlow, and the projects have users AWS and Microsoft. David Aronchick is the head of open source machine learning strategy at Microsoft, and he joins the show to talk about the problems that Kubeflow solves for developers, and the evolving strategies for cloud providers. David was previously on the show when he worked at Google, and in this episode he provides some useful discussion about how open source software presents a great opportunity for the cloud providers to collaborate with each other in a positive sum relationship. The post <a href="https://softwareengineeringdaily.com/2019/01/25/kubeflow-tensorflow-on-kubernetes-with-david-aronchick/" rel="nofollow" target="_blank">Kubeflow: TensorFlow on Kubernetes with David Aronchick</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

Kubeflow: TensorFlow on Kubernetes with David Aronchick

Greg Corrado

“You don’t mind if failures slow things down, but its very important that failures do not stop forward progress.” TensorFlow is an open source machine learning library intended to bring large-scale, distributed machine learning and deep learning to everyone. Google recently released the framework to the public as a second-generation API, having learned from the successes and failures of DistBelief. Greg Corrado is a senior research scientist and tech lead at Google, where he focuses on the research areas of machine intelligence, machine perception and natural language processing. Questions  From the end-user’s point of view, how does Smart Reply work? How can teams blend research and engineering to make better products? How did the DistBelief project shape Tensor Flow? How does Tensor Flow differ from streaming frameworks that are more generalized like Spark or Storm? Why would I want to do machine learning on my phone? How is Tensor Flow fault tolerant? What are things the open source community should dive into in Tensor Flow, to fix and improve it?  Links  TensorFlow Computer, respond to this email. Bridging Data Science and Engineering with Greg Lamp Greg’s Research Page  Sponsors Hired.com is the job marketplace for software engineers. Go to hired.com/softwareengineeringdaily to get a $600 bonus upon landing a job through Hired. Digital Ocean is the simplest cloud hosting provider. Use promo code SEDAILY for $10 in free credit.  The post TensorFlow with Greg Corrado appeared first on Software Engineering Daily.

<h3>“You don’t mind if failures slow things down, but its very important that failures do not stop forward progress.”</h3> TensorFlow is an open source machine learning library intended to bring large-scale, distributed machine learning and deep learning to everyone. Google recently released the framework to the public as a second-generation API, having learned from the successes and failures of <a href="http://papers.nips.cc/paper/4687-large-scale-distributed-deep-networks.pdf" rel="nofollow" target="_blank">DistBelief</a>. Greg Corrado is a senior research scientist and tech lead at Google, where he focuses on the research areas of machine intelligence, machine perception and natural language processing. <h3>Questions</h3> <ul> <li>From the end-user’s point of view, how does Smart Reply work?</li> <li>How can teams blend research and engineering to make better products?</li> <li>How did the DistBelief project shape Tensor Flow?</li> <li>How does Tensor Flow differ from streaming frameworks that are more generalized like Spark or Storm?</li> <li>Why would I want to do machine learning on my phone?</li> <li>How is Tensor Flow fault tolerant?</li> <li>What are things the open source community should dive into in Tensor Flow, to fix and improve it?</li> </ul> <h3>Links</h3> <ul> <li><a href="https://www.tensorflow.org/" rel="nofollow" target="_blank">TensorFlow</a></li> <li><a href="http://googleresearch.blogspot.com/2015/11/computer-respond-to-this-email.html" rel="nofollow" target="_blank">Computer, respond to this email.</a></li> <li><a href="http://softwaredaily.wpengine.com/2015/10/05/bridging-data-science-and-engineering-with-greg-lamp/" rel="nofollow" target="_blank">Bridging Data Science and Engineering with Greg Lamp</a></li> <li><a href="http://research.google.com/pubs/GregCorrado.html" rel="nofollow" target="_blank">Greg’s Research Page</a></li> </ul> <h3>Sponsors</h3> <a href="http://www.hired.com/softwareengineeringdaily" rel="nofollow" target="_blank">Hired.com</a> is the job marketplace for software engineers. Go to <a href="http://www.hired.com/softwareengineeringdaily" rel="nofollow" target="_blank">hired.com/softwareengineeringdaily</a> to get a $600 bonus upon landing a job through Hired. <a href="https://www.digitalocean.com/?utm_medium=podcasts&amp;utm_source=sedaily&amp;utm_campaign=sedaily" rel="nofollow" target="_blank">Digital Ocean</a> is the simplest cloud hosting provider. Use promo code SEDAILY for $10 in free credit. <a href="https://www.digitalocean.com/?utm_medium=podcasts&amp;utm_source=sedaily&amp;utm_campaign=sedaily" rel="nofollow" target="_blank"></a> The post <a href="https://softwareengineeringdaily.com/2015/12/15/tensorflow-with-greg-corrado/" rel="nofollow" target="_blank">TensorFlow with Greg Corrado</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

TensorFlow with Greg Corrado

Xander Dunn

A hedge fund is a collection of investors that make bets on the future. The “hedge” refers to the fact that the investors often try to diversify their strategies so that the direction of their bets are less correlated, and they can be successful in a variety of future scenarios. Engineering-focused hedge funds have used what might be called “machine learning” for a long time to predict what will happen in the future. Numerai is a hedge fund that crowdsources its investment strategies by allowing anyone to train models against Numerai’s data. A model that succeeds in a simulated environment will be adopted by Numerai and used within its real money portfolio. The engineers who create the models are rewarded in proportion to how well the models perform. Xander Dunn is a software engineer at Numerai and in this episode he explains what a hedge fund is, why the traditional strategies are not optimal, and how Numerai creates the right incentive structure to crowdsource market intelligence. This interview was fun and thought provoking–Numerai is one of those companies that makes me very excited about the future. The post Hedge Fund Artificial Intelligence with Xander Dunn appeared first on Software Engineering Daily.

A hedge fund is a collection of investors that make bets on the future. The “hedge” refers to the fact that the investors often try to diversify their strategies so that the direction of their bets are less correlated, and they can be successful in a variety of future scenarios. Engineering-focused hedge funds have used what might be called “machine learning” for a long time to predict what will happen in the future. Numerai is a hedge fund that crowdsources its investment strategies by allowing anyone to train models against Numerai’s data. A model that succeeds in a simulated environment will be adopted by Numerai and used within its real money portfolio. The engineers who create the models are rewarded in proportion to how well the models perform. Xander Dunn is a software engineer at Numerai and in this episode he explains what a hedge fund is, why the traditional strategies are not optimal, and how Numerai creates the right incentive structure to crowdsource market intelligence. This interview was fun and thought provoking–Numerai is one of those companies that makes me very excited about the future. The post <a href="https://softwareengineeringdaily.com/2017/04/03/hedge-fund-artificial-intelligence-with-xander-dunn/" rel="nofollow" target="_blank">Hedge Fund Artificial Intelligence with Xander Dunn</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

Hedge Fund Artificial Intelligence with Xander Dunn

George Hotz

In the smartphone market there are two dominant operating systems: one closed source (iPhone) and one open source (Android). The market for self-driving cars could play out the same way, with a company like Tesla becoming the closed source iPhone of cars, and a company like Comma.ai developing the open source Android of self-driving cars. George Hotz is the CEO of Comma.ai. Comma makes hardware devices that allow users with “normal” cars to be augmented with advanced cruise control and lane assist features. This means you can take your own car–for example, a Toyota Prius–and outfit your car to have something similar to the Tesla Autopilot. Comma’s hardware devices cost under $1000 to order online. George joins the show to explain how the Comma hardware and software stack works in detail–from the low level interface with a car’s CAN bus to the high level machine learning infrastructure.  Users who purchase the Comma.ai hardware drive around with a camera facing the front of their windshield. This video is used to orient the state of the car in space. The video from that camera also gets saved and uploaded to Comma’s servers. Comma can use this video together with labeled events from the user’s driving experience to crowdsource their model for self-driving. For example, if a user is driving down a long stretch of highway, and they turn on the Comma.ai driving assistance, the car will start driving itself and the video capture will begin. If the car begins to swerve into another lane, the user will take over for the car and the Comma system will disengage. This “disengagement” event gets labeled as such, and when that data makes it back to Comma’s servers, Comma can use the data to update their models. George is very good at explaining complex engineering topics, and is also quite entertaining and open to discussing the technology as well as other competitors in the autonomous car space. I have not been able to get many other people on the show to talk about autonomous cars, so this was quite refreshing! I hope to do more in the future. The post Self-Driving Engineering with George Hotz appeared first on Software Engineering Daily.

In the smartphone market there are two dominant operating systems: one closed source (iPhone) and one open source (Android). The market for self-driving cars could play out the same way, with a company like Tesla becoming the closed source iPhone of cars, and a company like <a href="http://Comma.ai" rel="nofollow" target="_blank">Comma.ai</a> developing the open source Android of self-driving cars. George Hotz is the CEO of <a href="http://Comma.ai" rel="nofollow" target="_blank">Comma.ai</a>. Comma makes hardware devices that allow users with “normal” cars to be augmented with advanced cruise control and lane assist features. This means you can take your own car–for example, a Toyota Prius–and outfit your car to have something similar to the Tesla Autopilot. Comma’s hardware devices cost under $1000 to order online. George joins the show to explain how the Comma hardware and software stack works in detail–from the low level interface with a car’s CAN bus to the high level machine learning infrastructure. Users who purchase the <a href="http://Comma.ai" rel="nofollow" target="_blank">Comma.ai</a> hardware drive around with a camera facing the front of their windshield. This video is used to orient the state of the car in space. The video from that camera also gets saved and uploaded to Comma’s servers. Comma can use this video together with labeled events from the user’s driving experience to crowdsource their model for self-driving. For example, if a user is driving down a long stretch of highway, and they turn on the <a href="http://Comma.ai" rel="nofollow" target="_blank">Comma.ai</a> driving assistance, the car will start driving itself and the video capture will begin. If the car begins to swerve into another lane, the user will take over for the car and the Comma system will disengage. This “disengagement” event gets labeled as such, and when that data makes it back to Comma’s servers, Comma can use the data to update their models. George is very good at explaining complex engineering topics, and is also quite entertaining and open to discussing the technology as well as other competitors in the autonomous car space. I have not been able to get many other people on the show to talk about autonomous cars, so this was quite refreshing! I hope to do more in the future. The post <a href="https://softwareengineeringdaily.com/2018/08/08/self-driving-engineering-with-george-hotz/" rel="nofollow" target="_blank">Self-Driving Engineering with George Hotz</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

Self-Driving Engineering with George Hotz

Will Constable

Deep learning allows engineers to build models that can make decisions based on training data. These models improve over time using stochastic gradient descent. When a model gets big enough, the training must be broken up across multiple machines. Two strategies for doing this are “model parallelism” which divides the model across machines and “data parallelism” which divides the data across multiple copies of the model. Distributed deep learning brings together two advanced software engineering concepts: distributed systems and deep learning. In this episode, Will Constable, the head of distributed deep learning algorithms at Intel Nervana, joins the show to give us a refresher on deep learning and explain how to parallelize training a model.  Full disclosure: Intel is a sponsor of Software Engineering Daily, and if you want to find out more about Intel Nervana including other interviews and job postings, go to softwareengineeringdaily.com/intel. Intel Nervana is looking for great engineers at all levels of the stack, and in this episode we’ll dive into some of the problems the Intel Nervana team is solving. Related episodes about machine learning can be found here. The post Distributed Deep Learning with Will Constable appeared first on Software Engineering Daily.

Deep learning allows engineers to build models that can make decisions based on training data. These models improve over time using stochastic gradient descent. When a model gets big enough, the training must be broken up across multiple machines. Two strategies for doing this are “model parallelism” which divides the model across machines and “data parallelism” which divides the data across multiple copies of the model. Distributed deep learning brings together two advanced software engineering concepts: distributed systems and deep learning. In this episode, Will Constable, the head of distributed deep learning algorithms at Intel Nervana, joins the show to give us a refresher on deep learning and explain how to parallelize training a model. Full disclosure: Intel is a sponsor of Software Engineering Daily, and if you want to find out more about Intel Nervana including other interviews and job postings, go to <a href="http://softwareengineeringdaily.com/intel" rel="nofollow" target="_blank">softwareengineeringdaily.com/intel</a>. Intel Nervana is looking for great engineers at all levels of the stack, and in this episode we’ll dive into some of the problems the Intel Nervana team is solving. <a href="https://softwareengineeringdaily.com/category/machine-learning/" rel="nofollow" target="_blank">Related episodes about machine learning can be found here.</a> The post <a href="https://softwareengineeringdaily.com/2017/06/14/distributed-deep-learning-with-will-constable/" rel="nofollow" target="_blank">Distributed Deep Learning with Will Constable</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

Distributed Deep Learning with Will Constable

Adam Gibson

Deep learning uses neural networks to identify patterns. Neural networks allow us to sequence “layers” of computing, with each layer using learning algorithms such as unsupervised learning, supervised learning, and reinforcement learning. Deep learning has taken off in the last few years, but it has been around for much longer. Adam Gibson founded Skymind, the company behind Deeplearning4j. Deeplearning4j is a distributed deep learning library for Scala and Java. It integrates with Hadoop and Spark, and is specifically designed to run in business environments on distributed GPUs and CPUs. Adam joins the show today to discuss the history and future of deep learning. The post Deep Learning with Adam Gibson appeared first on Software Engineering Daily.

Deep learning uses neural networks to identify patterns. Neural networks allow us to sequence “layers” of computing, with each layer using learning algorithms such as unsupervised learning, supervised learning, and reinforcement learning. Deep learning has taken off in the last few years, but it has been around for much longer. Adam Gibson founded Skymind, the company behind Deeplearning4j. Deeplearning4j is a distributed deep learning library for Scala and Java. It integrates with Hadoop and Spark, and is specifically designed to run in business environments on distributed GPUs and CPUs. Adam joins the show today to discuss the history and future of deep learning. The post <a href="https://softwareengineeringdaily.com/2017/02/10/deep-learning-with-adam-gibson/" rel="nofollow" target="_blank">Deep Learning with Adam Gibson</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

Deep Learning with Adam Gibson

Current infrastructure makes it difficult for data scientists to share analytical models with the software engineers who need to integrate them. Yhat is an enterprise software company tackling the challenge of how data science gets done. Their products enable companies and users to easily deploy data science environments and translate analytical models into production code. Greg Lamp is the Co-founder and CTO of Yhat and previously worked as a product manager in financial services. Yhat was part of the Y Combinator winter 2015 class. Questions  At a software company, what is the typical relationship between data scientists and software engineers? Does Yhat turn data scientists into HTTP endpoints? What was the most counterintuitive advice you received at Y-Combinator? What is the moonshot goal for Yhat? Is it easier to teach data science to an engineer or engineering to a data scientist?  Links  Yhat’s Products Yhat Blog Greg’s Website Beer Recommender Talk  The post Bridging Data Science and Engineering with Greg Lamp appeared first on Software Engineering Daily.

<h3>Current infrastructure makes it difficult for data scientists to share analytical models with the software engineers who need to integrate them.</h3> Yhat is an enterprise software company tackling the challenge of how data science gets done. Their products enable companies and users to easily deploy data science environments and translate analytical models into production code. Greg Lamp is the Co-founder and CTO of Yhat and previously worked as a product manager in financial services. Yhat was part of the Y Combinator winter 2015 class. <h3>Questions</h3> <ul> <li>At a software company, what is the typical relationship between data scientists and software engineers?</li> <li>Does Yhat turn data scientists into HTTP endpoints?</li> <li>What was the most counterintuitive advice you received at Y-Combinator?</li> <li>What is the moonshot goal for Yhat?</li> <li>Is it easier to teach data science to an engineer or engineering to a data scientist?</li> </ul> <h3>Links</h3> <ul> <li><a href="https://www.yhathq.com/docs" rel="nofollow" target="_blank">Yhat’s Products</a></li> <li><a href="http://blog.yhathq.com/" rel="nofollow" target="_blank">Yhat Blog</a></li> <li><a href="http://www.yaksis.com/" rel="nofollow" target="_blank">Greg’s Website</a></li> <li><a href="https://www.youtube.com/watch?v=zlOPX6f3rcc" rel="nofollow" target="_blank">Beer Recommender Talk</a></li> </ul> The post <a href="https://softwareengineeringdaily.com/2015/10/05/bridging-data-science-and-engineering-with-greg-lamp/" rel="nofollow" target="_blank">Bridging Data Science and Engineering with Greg Lamp</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

Bridging Data Science and Engineering with Greg Lamp

Krish Chaudhury

RECENT UPDATES: Podsheets is our open source set of tools for managing podcasts and podcast businesses New version of Software Daily, our app and ad-free subscription service Software Daily is looking for help with Android engineering, QA, machine learning, and more FindCollabs Hackathon has ended–winners will probably be announced by the time this episode airs; we will be announcing our next hackathon in a few weeks, so stay tuned Drishti is a company focused on improving manufacturing workflows using computer vision. A manufacturing environment consists of assembly lines. A line is composed of sequential stations along that manufacturing line. At each station on the assembly line, a worker performs an operation on the item that is being manufactured. This type of workflow is used for the manufacturing of cars, laptops, stereo equipment, and many other technology products. With Drishti, the manufacturing process is augmented by adding a camera at each station. Camera footage is used to train a machine learning model for each station on the assembly line. That machine learning model is used to ensure the accuracy and performance of each task that is being conducted on the assembly line. Krish Chaudhury is the CTO at Drishti. From 2005 to 2015 he led image processing and computer vision projects at Google before joining Flipkart, where he worked on image science and deep learning for another four years. Krish had spent more than twenty years working on image and vision related problems when he co-founded Drishti. In today’s episode, we discuss the science and application of computer vision, as well as the future of manufacturing technology and the business strategy of Drishti. The post Drishti: Deep Learning for Manufacturing with Krish Chaudhury appeared first on Software Engineering Daily.

RECENT UPDATES: <a href="https://findcollabs.com/project/IQNarf2tJ8Un4esfoXck" rel="nofollow" target="_blank">Podsheets is our open source set of tools for managing podcasts and podcast businesses</a> <a href="http://softwaredaily.com" rel="nofollow" target="_blank">New version of Software Daily, our app and ad-free subscription service</a> <a href="https://findcollabs.com/project/TQQCLSL2KYLq9jS7gxBq" rel="nofollow" target="_blank">Software Daily is looking for help with Android engineering, QA, machine learning, and more</a> <a href="http://findcollabs.com/hackathon" rel="nofollow" target="_blank">FindCollabs Hackathon has ended–winners will probably be announced by the time this episode airs; we will be announcing our next hackathon in a few weeks, so stay tuned</a> Drishti is a company focused on improving manufacturing workflows using computer vision. A manufacturing environment consists of assembly lines. A line is composed of sequential stations along that manufacturing line. At each station on the assembly line, a worker performs an operation on the item that is being manufactured. This type of workflow is used for the manufacturing of cars, laptops, stereo equipment, and many other technology products. With Drishti, the manufacturing process is augmented by adding a camera at each station. Camera footage is used to train a machine learning model for each station on the assembly line. That machine learning model is used to ensure the accuracy and performance of each task that is being conducted on the assembly line. Krish Chaudhury is the CTO at Drishti. From 2005 to 2015 he led image processing and computer vision projects at Google before joining Flipkart, where he worked on image science and deep learning for another four years. Krish had spent more than twenty years working on image and vision related problems when he co-founded Drishti. In today’s episode, we discuss the science and application of computer vision, as well as the future of manufacturing technology and the business strategy of Drishti. The post <a href="https://softwareengineeringdaily.com/2019/04/17/drishti-deep-learning-for-manufacturing-with-krish-chaudhury/" rel="nofollow" target="_blank">Drishti: Deep Learning for Manufacturing with Krish Chaudhury</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

Drishti: Deep Learning for Manufacturing with Krish Chaudhury

Andreas Mueller

Scikit-learn is a set of machine learning tools in Python that provides easy-to-use interfaces for building predictive models. In a previous episode with Per Harald Borgen about Machine Learning For Sales, he illustrated how easy it is to get up and running and productive with scikit-learn, even if you are not a machine learning expert. Srini Kadamati hosts today’s show and interviews Andreas Mueller, a core committer to scikit-learn. Srini and Andreas discuss the background and implementation of scikit-learn and walk through some prototypical workflows for using it.  The post Scikit-learn with Andreas Mueller appeared first on Software Engineering Daily.

Scikit-learn is a set of machine learning tools in Python that provides easy-to-use interfaces for building predictive models. In a previous episode with Per Harald Borgen about Machine Learning For Sales, he illustrated how easy it is to get up and running and productive with scikit-learn, even if you are not a machine learning expert. Srini Kadamati hosts today’s show and interviews Andreas Mueller, a core committer to scikit-learn. Srini and Andreas discuss the background and implementation of scikit-learn and walk through some prototypical workflows for using it. The post <a href="https://softwareengineeringdaily.com/2016/09/27/scikit-learn-with-andreas-mueller/" rel="nofollow" target="_blank">Scikit-learn with Andreas Mueller</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

Scikit-learn with Andreas Mueller

Jake Vanderplas

Data visualization tools are required to translate the findings of data scientists into charts, graphs, and pictures. Understanding how to utilize these tools and display data is necessary for a data scientist to communicate with people in other domains. In this episode, Srini Kadamati hosts a discussion with Jake VanderPlas about the Python ecosystem for data science and the different attempts at creating a data visualization library. Jake VanderPlas is the Director of Research for Physical Sciences at the University of Washington’s eScience institute, where he also received his PhD in Astronomy. In addition to contributing to many Python data science libraries like scikit-learn, scipy, numpy, and matplotlib, he’s written multiple books that have been published by O’Reilly and has given many talks on data science tools and techniques. He’s also the co-creator of the Altair project, which is a declarative data visualization library for Python built on the Vega-Lite visualization grammar. The post Python Data Visualization with Jake VanderPlas appeared first on Software Engineering Daily.

Data visualization tools are required to translate the findings of data scientists into charts, graphs, and pictures. Understanding how to utilize these tools and display data is necessary for a data scientist to communicate with people in other domains. In this episode, Srini Kadamati hosts a discussion with Jake VanderPlas about the Python ecosystem for data science and the different attempts at creating a data visualization library. Jake VanderPlas is the Director of Research for Physical Sciences at the University of Washington’s eScience institute, where he also received his PhD in Astronomy. In addition to contributing to many Python data science libraries like scikit-learn, scipy, numpy, and matplotlib, he’s written multiple books that have been published by O’Reilly and has given many talks on data science tools and techniques. He’s also the co-creator of the Altair project, which is a declarative data visualization library for Python built on the Vega-Lite visualization grammar. The post <a href="https://softwareengineeringdaily.com/2017/01/16/python-data-visualization-with-jake-vanderplas/" rel="nofollow" target="_blank">Python Data Visualization with Jake VanderPlas</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

Python Data Visualization with Jake VanderPlas

Milena Marinova

The applications that demand deep learning range from self-driving cars to healthcare, but the way that models are developed and trained is similar. A model is trained in the cloud and deployed to a device. The device engages with the real world, gathering more data. That data is sent back to the cloud, where it can improve the model. From the processor level to the software frameworks at the top of the stack, the impact of deep learning is so significant that it is driving changes everywhere. At the hardware level, new chips are being designed to perform the matrix calculations at the heart of a neural net. At the software level, programmers are empowered by new frameworks like Neon and TensorFlow. In between the programmer and the hardware, middleware can transform software models into representations that can execute with better performance. Milena Marinova is the senior director of AI solutions at the Intel AI products group, and joins the show today to talk about modern applications of machine learning and how those translate into Intel’s business strategy around hardware, software, and cloud.  From September 18-20, Milena is attending the O’Reilly AI Conference, hosted by Intel Nervana and O’Reilly. Full disclosure: Intel is a sponsor of Software Engineering Daily. Question of the Week: What is your favorite continuous delivery or continuous integration tool? Email jeff@softwareengineeringdaily.com and a winner will be chosen at random to receive a Software Engineering Daily hoodie.  Show Notes Data Skeptic podcast: Generative Adversarial Networks The post Deep Learning Systems with Milena Marinova appeared first on Software Engineering Daily.

The applications that demand deep learning range from self-driving cars to healthcare, but the way that models are developed and trained is similar. A model is trained in the cloud and deployed to a device. The device engages with the real world, gathering more data. That data is sent back to the cloud, where it can improve the model. From the processor level to the software frameworks at the top of the stack, the impact of deep learning is so significant that it is driving changes everywhere. At the hardware level, new chips are being designed to perform the matrix calculations at the heart of a neural net. At the software level, programmers are empowered by new frameworks like Neon and TensorFlow. In between the programmer and the hardware, middleware can transform software models into representations that can execute with better performance. Milena Marinova is the senior director of AI solutions at the Intel AI products group, and joins the show today to talk about modern applications of machine learning and how those translate into Intel’s business strategy around hardware, software, and cloud. <a href="https://conferences.oreilly.com/artificial-intelligence/ai-ca" rel="nofollow" target="_blank">From September 18-20, Milena is attending the O’Reilly AI Conference, hosted by Intel Nervana and O’Reilly.</a> Full disclosure: Intel is a sponsor of Software Engineering Daily. Question of the Week: What is your favorite continuous delivery or continuous integration tool? Email jeff@softwareengineeringdaily.com and a winner will be chosen at random to receive a Software Engineering Daily hoodie.  <h2>Show Notes</h2> Data Skeptic podcast: <a href="https://dataskeptic.com/blog/episodes/2017/generative-adversarial-networks" rel="nofollow" target="_blank">Generative Adversarial Networks</a> The post <a href="https://softwareengineeringdaily.com/2017/09/19/deep-learning-systems-with-milena-marinova/" rel="nofollow" target="_blank">Deep Learning Systems with Milena Marinova</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

Deep Learning Systems with Milena Marinova

Stock traders have access to high volumes of information to help them make decisions on whether to buy an asset. A trader who is considering buying a share of Google stock can find charts, reports, and statistical tools to help with their decision. There are a variety of machine learning products to help a technical investor create models of how a stock price might change in the future. Real estate investors do not have access to the same data and tooling. Most people who invest in apartment buildings are using a combination of experience, news, and basic reports. Real estate data is very different from stock data. Real estate assets are not fungible–each one is arguably unique from all others, whereas one share of Google stock is the same as another share. But there are commonalities between real estate assets. Just like collaborative filtering can be applied to find a new movie that is similar to the ones you have watched on Netflix, comparable analysis can be used to find an apartment building that is very similar to another apartment building which recently appreciated in asset value. Skyline.ai is a company that is building tools and machine learning models for real estate investors. Or Hiltch is the CTO at Skyline.ai and he joins the show to explain how to apply machine learning to real estate investing. He also describes the mostly serverless architecture of the company. This is one of the first companies we have talked to that is so heavily on managed services and functions-as-a-service.   Show Notes  Skyline AI – Technology Skyline AI – Investment Platform Skyline AI raises $3M from Sequoia Capital to help real estate investors make better decisions | TechCrunch Skyline AI raises $18M Series A for its machine learning-based real estate investment tech | TechCrunch Israeli real estate tech startup Skyline AI raises $18 mln | Reuters Or Hiltch: Serverless ETLs at Skyline AI // Serverless Days TLV – YouTube Machine Learning in Real Estate – Trends and Applications Presented What are some machine learning applications in the real estate space? – Quora The Potential of Machine Learning Real Estate Valuation Models (5 mins) | Cornell Real Estate Review  The post Real Estate Machine Learning with Or Hiltch appeared first on Software Engineering Daily.

Stock traders have access to high volumes of information to help them make decisions on whether to buy an asset. A trader who is considering buying a share of Google stock can find charts, reports, and statistical tools to help with their decision. There are a variety of machine learning products to help a technical investor create models of how a stock price might change in the future. Real estate investors do not have access to the same data and tooling. Most people who invest in apartment buildings are using a combination of experience, news, and basic reports. Real estate data is very different from stock data. Real estate assets are not fungible–each one is arguably unique from all others, whereas one share of Google stock is the same as another share. But there are commonalities between real estate assets. Just like collaborative filtering can be applied to find a new movie that is similar to the ones you have watched on Netflix, comparable analysis can be used to find an apartment building that is very similar to another apartment building which recently appreciated in asset value. <a href="http://Skyline.ai" rel="nofollow" target="_blank">Skyline.ai</a> is a company that is building tools and machine learning models for real estate investors. Or Hiltch is the CTO at <a href="http://Skyline.ai" rel="nofollow" target="_blank">Skyline.ai</a> and he joins the show to explain how to apply machine learning to real estate investing. He also describes the mostly serverless architecture of the company. This is one of the first companies we have talked to that is so heavily on managed services and functions-as-a-service. <h2>Show Notes</h2> <ul> <li><a href="https://www.skyline.ai/technology.html" rel="nofollow" target="_blank">Skyline AI – Technology</a></li> <li><a href="https://www.skyline.ai/platform.html" rel="nofollow" target="_blank">Skyline AI – Investment Platform</a></li> <li><a href="https://techcrunch.com/2018/03/22/skyline-ai-raises-3m-from-sequoia-capital-to-help-real-estate-investors-make-better-decisions/" rel="nofollow" target="_blank">Skyline AI raises $3M from Sequoia Capital to help real estate investors make better decisions | TechCrunch</a></li> <li><a href="https://techcrunch.com/2018/07/31/skyline-ai-raises-18m-series-a-for-its-machine-learning-based-real-estate-investment-tech/" rel="nofollow" target="_blank">Skyline AI raises $18M Series A for its machine learning-based real estate investment tech | TechCrunch</a></li> <li><a href="https://www.reuters.com/article/tech-skyline-ai-fundraising/israeli-real-estate-tech-startup-skyline-ai-raises-18-mln-idUSL5N1UI06E?feedType=RSS&amp;feedName=companyNews" rel="nofollow" target="_blank">Israeli real estate tech startup Skyline AI raises $18 mln | Reuters</a></li> <li><a href="https://www.youtube.com/watch?v=28NSgSaq2XE" rel="nofollow" target="_blank">Or Hiltch: Serverless ETLs at Skyline AI // Serverless Days TLV – YouTube</a></li> <li><a href="https://www.techemergence.com/machine-learning-in-real-estate-trends-and-applications/" rel="nofollow" target="_blank">Machine Learning in Real Estate – Trends and Applications Presented</a></li> <li><a href="https://www.quora.com/What-are-some-machine-learning-applications-in-the-real-estate-space" rel="nofollow" target="_blank">What are some machine learning applications in the real estate space? – Quora</a></li> <li><a href="https://blog.realestate.cornell.edu/2018/03/28/machine-learning/" rel="nofollow" target="_blank">The Potential of Machine Learning Real Estate Valuation Models (5 mins) | Cornell Real Estate Review</a></li> </ul> The post <a href="https://softwareengineeringdaily.com/2018/09/11/real-estate-machine-learning-with-or-hiltch/" rel="nofollow" target="_blank">Real Estate Machine Learning with Or Hiltch</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

Real Estate Machine Learning with Or Hiltch

Xin Wang

Training a deep learning model involves operations over tensors. A tensor is a multi-dimensional array of numbers. For several years, GPUs were used for these linear algebra calculations. That’s because graphics chips are built to efficiently process matrix operations. Tensor processing consists of linear algebra operations that are similar in some ways to graphics processing–but not identical. Deep learning workloads do not run as efficiently on these conventional GPUs as they would on specialized chips, built specifically for deep learning. In order to train deep learning models faster, new hardware needs to be designed with tensor processing in mind. Xin Wang is a data scientist with the artificial intelligence products group at Intel. He joins today’s show to discuss deep learning hardware and Flexpoint, a way to improve the efficiency of space that tensors take up on a chip. Xin presented his work at NIPS, the Neural Information Processing Systems conference, and we talked about what he saw at NIPs that excited him. Full disclosure: Intel, where Xin works, is a sponsor of Software Engineering Daily. The post Deep Learning Hardware with Xin Wang appeared first on Software Engineering Daily.

Training a deep learning model involves operations over tensors. A tensor is a multi-dimensional array of numbers. For several years, GPUs were used for these linear algebra calculations. That’s because graphics chips are built to efficiently process matrix operations. Tensor processing consists of linear algebra operations that are similar in some ways to graphics processing–but not identical. Deep learning workloads do not run as efficiently on these conventional GPUs as they would on specialized chips, built specifically for deep learning. In order to train deep learning models faster, new hardware needs to be designed with tensor processing in mind. Xin Wang is a data scientist with the artificial intelligence products group at Intel. He joins today’s show to discuss deep learning hardware and Flexpoint, a way to improve the efficiency of space that tensors take up on a chip. Xin presented his work at NIPS, the Neural Information Processing Systems conference, and we talked about what he saw at NIPs that excited him. Full disclosure: Intel, where Xin works, is a sponsor of Software Engineering Daily. The post <a href="https://softwareengineeringdaily.com/2018/01/29/deep-learning-hardware-with-xin-wang/" rel="nofollow" target="_blank">Deep Learning Hardware with Xin Wang</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

Deep Learning Hardware with Xin Wang

Self-driving cars are here. Fully autonomous systems like Waymo are being piloted in less complex circumstances. Human-in-the-loop systems like Tesla Autopilot navigate drivers when it is safe to do so, and lets the human take control in ambiguous circumstances. Computers are great at memorization, but not yet great at reasoning. We cannot enumerate to a computer every single circumstance that a car might find itself in. The computer needs to perceive its surroundings, plan how to take action, execute control over the situation, and respond to changing circumstances inside and outside of the car. Lex Fridman has worked on autonomous vehicles with companies like Google and Tesla. He recently taught a class on deep learning for semi-autonomous vehicles at MIT, which is freely available online. There was so much ground to cover in this conversation. Most of the conversation was higher level. How do you even approach the problem? What is the hardware and software architecture of a car? I enjoyed talking to Lex, and if you want to hear more from him check out his podcast Take It Uneasy, which is about jiu jitsu, judo, wrestling, and learning. The post Self-Driving Deep Learning with Lex Fridman appeared first on Software Engineering Daily.

Self-driving cars are here. Fully autonomous systems like Waymo are being piloted in less complex circumstances. Human-in-the-loop systems like Tesla Autopilot navigate drivers when it is safe to do so, and lets the human take control in ambiguous circumstances. Computers are great at memorization, but not yet great at reasoning. We cannot enumerate to a computer every single circumstance that a car might find itself in. The computer needs to perceive its surroundings, plan how to take action, execute control over the situation, and respond to changing circumstances inside and outside of the car. Lex Fridman has worked on autonomous vehicles with companies like Google and Tesla. He recently taught a class on deep learning for semi-autonomous vehicles at MIT, which is freely available online. There was so much ground to cover in this conversation. Most of the conversation was higher level. How do you even approach the problem? What is the hardware and software architecture of a car? I enjoyed talking to Lex, and if you want to hear more from him check out his podcast <a href="http://takeituneasy.com/" rel="nofollow" target="_blank">Take It Uneasy</a>, which is about jiu jitsu, judo, wrestling, and learning. The post <a href="https://softwareengineeringdaily.com/2017/07/28/self-driving-deep-learning-with-lex-fridman/" rel="nofollow" target="_blank">Self-Driving Deep Learning with Lex Fridman</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

Self-Driving Deep Learning with Lex Fridman

Alexandr Wang

The easiest way to train a computer to recognize a picture of a cat is to show the computer a million labeled images of cats. The easiest way to train a computer to recognize a stop sign is to show the computer a million labeled stop signs.  Supervised machine learning systems require labeled data. Today, most of that labeling needs to be done by humans. When a large tech company decides to “build a machine learning model,” that often requires a massive amount of effort to get labeled data.  Hundreds of thousands of knowledge workers around the world earn their income from labeling tasks. An example task might be “label all of the pedestrians in this intersection.” You receive a picture of a crowded intersection, and your task is to circle all the pedestrians. You have now created a piece of labeled data. Scale API is a company that turns API requests into human tasks. Their most recent release is an API for labeling data that has been generated from sensors. As self-driving cars emerge onto our streets, the sensors on these cars generate LIDAR, radar, and camera data. The cars will interpret that data in real-time using their machine learning models, and then they will send that data to the cloud so that the data can be processed offline to improve the machine learning models of every car on the road. The first step in that processing pipeline is the labeling–which is the focus of today’s conversation. Alexandr Wang is the CEO of Scale, and he joins the show to discuss self-driving cars, labeling, and the company he co-founded. A few notes before we get started. We just launched the Software Daily, job board. To check it out, go to softwaredaily.com/jobs. You can post jobs, you can apply for jobs, and it’s all free. If you are looking to hire, or looking for a job, I recommend checking it out. And if you are looking for an internship, you can use the job board to apply for an internship at Software Engineering Daily. Also, Meetups for Software Engineering Daily are being planned! Go to softwareengineeringdaily.com/meetup if you want to register for an upcoming Meetup. In March, I’ll be visiting Datadog in New York and Hubspot in Boston, and in April I’ll be at Telesign in LA. The post Scale Self-Driving with Alexandr Wang appeared first on Software Engineering Daily.

The easiest way to train a computer to recognize a picture of a cat is to show the computer a million labeled images of cats. The easiest way to train a computer to recognize a stop sign is to show the computer a million labeled stop signs. Supervised machine learning systems require labeled data. Today, most of that labeling needs to be done by humans. When a large tech company decides to “build a machine learning model,” that often requires a massive amount of effort to get labeled data. Hundreds of thousands of knowledge workers around the world earn their income from labeling tasks. An example task might be “label all of the pedestrians in this intersection.” You receive a picture of a crowded intersection, and your task is to circle all the pedestrians. You have now created a piece of labeled data. Scale API is a company that turns API requests into human tasks. Their most recent release is an API for labeling data that has been generated from sensors. As self-driving cars emerge onto our streets, the sensors on these cars generate LIDAR, radar, and camera data. The cars will interpret that data in real-time using their machine learning models, and then they will send that data to the cloud so that the data can be processed offline to improve the machine learning models of every car on the road. The first step in that processing pipeline is the labeling–which is the focus of today’s conversation. Alexandr Wang is the CEO of Scale, and he joins the show to discuss self-driving cars, labeling, and the company he co-founded. A few notes before we get started. We just launched the Software Daily, job board. To check it out, go to <a href="http://softwaredaily.com/jobs" rel="nofollow" target="_blank">softwaredaily.com/jobs</a>. You can post jobs, you can apply for jobs, and it’s all free. If you are looking to hire, or looking for a job, I recommend checking it out. And if you are looking for an internship, you can use the job board to apply for an internship at Software Engineering Daily. Also, Meetups for Software Engineering Daily are being planned! Go to <a href="https://softwareengineeringdaily.com/meetup" rel="nofollow" target="_blank">softwareengineeringdaily.com/meetup</a> if you want to register for an upcoming Meetup. In March, I’ll be visiting Datadog in New York and Hubspot in Boston, and in April I’ll be at Telesign in LA. The post <a href="https://softwareengineeringdaily.com/2018/02/27/scale-self-driving-with-alexandr-wang/" rel="nofollow" target="_blank">Scale Self-Driving with Alexandr Wang</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

Scale Self-Driving with Alexandr Wang

Zayd Enam

Machine learning frameworks like Torch and TensorFlow have made the job of a machine learning engineer much easier. But machine learning is still hard. Debugging a machine learning model is a slow, messy process.  A bug in a machine learning model does not always mean a complete failure. Your model could continue to deliver usable results even in the presence of a mistaken implementation. Perhaps you made a mistake when cleaning your data, leading to an incorrectly trained model. It is a general rule in computer science that partial failures are harder to fix than complete failures. In this episode, Zayd Enam describes the different dimensions on which a machine learning model can develop an error. Zayd is a machine learning researcher at the Stanford AI Lab, so I also asked him about AI risk, job displacement, and academia versus industry. Show Notes Why ML is hard The post Machine Learning is Hard with Zayd Enam appeared first on Software Engineering Daily.

Machine learning frameworks like Torch and TensorFlow have made the job of a machine learning engineer much easier. But machine learning is still hard. Debugging a machine learning model is a slow, messy process. A bug in a machine learning model does not always mean a complete failure. Your model could continue to deliver usable results even in the presence of a mistaken implementation. Perhaps you made a mistake when cleaning your data, leading to an incorrectly trained model. It is a general rule in computer science that partial failures are harder to fix than complete failures. In this episode, Zayd Enam describes the different dimensions on which a machine learning model can develop an error. Zayd is a machine learning researcher at the Stanford AI Lab, so I also asked him about AI risk, job displacement, and academia versus industry. <h2>Show Notes</h2> <a href="http://ai.stanford.edu/~zayd/why-is-machine-learning-hard.html" rel="nofollow" target="_blank">Why ML is hard</a> The post <a href="https://softwareengineeringdaily.com/2017/02/16/machine-learning-is-hard-with-zayd-enam/" rel="nofollow" target="_blank">Machine Learning is Hard with Zayd Enam</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

Machine Learning is Hard with Zayd Enam

Adrian Colyer

Machines understand the world through mathematical representations. In order to train a machine learning model, we need to describe everything in terms of numbers.  Images, words, and sounds are too abstract for a computer. But a series of numbers is a representation that we can all agree on, whether we are a computer or a human. In recent shows, we have explored how to train machine learning models to understand images and video. Today, we explore words. You might be thinking–”isn’t a word easy to understand? Can’t you just take the dictionary definition?” A dictionary definition does not capture the richness of a word. Dictionaries do not give you a way to measure similarity between one word and all other words in a given language. Word2vec is a system for defining words in terms of the words that appear close to that word. For example, the sentence “Howard is sitting in a Starbucks cafe drinking a cup of coffee” gives an obvious indication that the words “cafe,” “cup,” and “coffee” are all related. With enough sentences like that, we can start to understand the entire language. Adrian Colyer is a venture capitalist with Accel, and blogs about technical topics such as word2vec. We talked about word2vec specifically, and the deep learning space more generally. We also explored how the rapidly improving tools around deep learning are changing the venture investment landscape. If you like this episode, we have done many other shows about machine learning with guests like Matt Zeiler, the founder of Clarif.ai and Francois Chollet, the creator of Keras. You can check out our back catalog by downloading the Software Engineering Daily app for iOS, where you can listen to all of our old episodes, and easily discover new topics that might interest you. You can upvote the episodes you like and get recommendations based on your listening history. With 600 episodes, it is hard to find the episodes that appeal to you, and we hope the app helps with that. Question of the Week: What is your favorite continuous delivery or continuous integration tool? Email jeff@softwareengineeringdaily.com and a winner will be chosen at random to receive a Software Engineering Daily hoodie.  The post Word2Vec with Adrian Colyer appeared first on Software Engineering Daily.

Machines understand the world through mathematical representations. In order to train a machine learning model, we need to describe everything in terms of numbers.  Images, words, and sounds are too abstract for a computer. But a series of numbers is a representation that we can all agree on, whether we are a computer or a human. In recent shows, we have explored how to train machine learning models to understand images and video. Today, we explore words. You might be thinking–”isn’t a word easy to understand? Can’t you just take the dictionary definition?” A dictionary definition does not capture the richness of a word. Dictionaries do not give you a way to measure similarity between one word and all other words in a given language. Word2vec is a system for defining words in terms of the words that appear close to that word. For example, the sentence “Howard is sitting in a Starbucks cafe drinking a cup of coffee” gives an obvious indication that the words “cafe,” “cup,” and “coffee” are all related. With enough sentences like that, we can start to understand the entire language. Adrian Colyer is a venture capitalist with Accel, and blogs about technical topics <a href="https://blog.acolyer.org/2016/04/21/the-amazing-power-of-word-vectors/" rel="nofollow" target="_blank">such as word2vec</a>. We talked about word2vec specifically, and the deep learning space more generally. We also explored how the rapidly improving tools around deep learning are changing the venture investment landscape. If you like this episode, we have done many other shows about machine learning with guests like Matt Zeiler, the founder of <a href="http://Clarif.ai" rel="nofollow" target="_blank">Clarif.ai</a> and Francois Chollet, the creator of Keras. You can check out our back catalog by downloading <a href="https://itunes.apple.com/us/app/software-engineering-daily-podcast-app/id1253734426?mt=8" rel="nofollow" target="_blank">the Software Engineering Daily app for iOS</a>, where you can listen to all of our old episodes, and easily discover new topics that might interest you. You can upvote the episodes you like and get recommendations based on your listening history. With 600 episodes, it is hard to find the episodes that appeal to you, and we hope the app helps with that. Question of the Week: What is your favorite continuous delivery or continuous integration tool? Email jeff@softwareengineeringdaily.com and a winner will be chosen at random to receive a Software Engineering Daily hoodie.  The post <a href="https://softwareengineeringdaily.com/2017/09/13/word2vec-with-adrian-colyer/" rel="nofollow" target="_blank">Word2Vec with Adrian Colyer</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

Word2Vec with Adrian Colyer

Andrey Akselrod

People.ai

A large sales organization has hundreds of sales people. Each of those sales people manages a set of accounts who they are trying to close sales deals on. Sales people are overseen by managers who ensure that the sales people are performing well. Directors and VPs ensure the scalability and health of the overall sales organization. The sales lifecycle mostly takes place within a piece of software called a CRM: customer relationship management. This tool documents the interactions between sales people and accounts. CRMs have been around for many years, and although CRM software is a useful repository of data, it does not fulfill all the needs of a salesperson. People.ai is a system of machine learning tools built around the sales tooling ecosystem. People.ai helps a sales organization avoid manual data entry, understand areas of potential improvement, and decide on who the highest value sales lead to pursue might be. Andrey Akselrod is the CTO At People.ai and he joins the show to discuss the potential applications of machine learning in the domain of sales, and the engineering work that his company has done. Sponsorship inquiries: sponsor@softwareengineeringdaily.com ANNOUNCEMENTS  FindCollabs is a place to find collaborators and build projects. We recently launched GitHub integrations. It’s easier than ever to find collaborators for your open source projects. And if you are looking for some people to start a project with, FindCollabs we have topic rooms that allow you to find other people who are interested in a particular technology, so that you can find people who are curious about React, or cryptocurrencies, or Kubernetes, or whatever you want to build with. Podsheets is an open source podcast hosting platform that we recently launched. We are building Podsheets with the learnings from Software Engineering Daily, and our goal is to be the best place to host and monetize your podcast. If you have been thinking about starting a podcast, check out podsheets.com.  New SEDaily app for iOS and for Android. It includes all 1000 of our old episodes, as well as related links, greatest hits, and topics. You can comment on episodes and have discussions with other members of the community. I’ll be commenting on each episode, so if you hear an episode that you have some commentary on, jump onto the app, or on SoftwareDaily.com to share your thoughts. And you can become a paid subscriber for ad free episodes at softwareengineeringdaily.com/subscribe. Altalogy is the company who has been developing much of the software for the newest app, and if you are looking for a company to help you with your mobile and web development, I recommend checking them out.    The post People.ai: Machine Learning for Sales with Andrey Akselrod appeared first on Software Engineering Daily.

A large sales organization has hundreds of sales people. Each of those sales people manages a set of accounts who they are trying to close sales deals on. Sales people are overseen by managers who ensure that the sales people are performing well. Directors and VPs ensure the scalability and health of the overall sales organization. The sales lifecycle mostly takes place within a piece of software called a CRM: customer relationship management. This tool documents the interactions between sales people and accounts. CRMs have been around for many years, and although CRM software is a useful repository of data, it does not fulfill all the needs of a salesperson. <a href="http://People.ai" rel="nofollow" target="_blank">People.ai</a> is a system of machine learning tools built around the sales tooling ecosystem. <a href="http://People.ai" rel="nofollow" target="_blank">People.ai</a> helps a sales organization avoid manual data entry, understand areas of potential improvement, and decide on who the highest value sales lead to pursue might be. Andrey Akselrod is the CTO At <a href="http://People.ai" rel="nofollow" target="_blank">People.ai</a> and he joins the show to discuss the potential applications of machine learning in the domain of sales, and the engineering work that his company has done. Sponsorship inquiries: <a href="mailto:sponsor@softwareengineeringdaily.com">sponsor@softwareengineeringdaily.com</a> <h2>ANNOUNCEMENTS</h2> <ul> <li><a href="https://findcollabs.com/" rel="nofollow" target="_blank">FindCollabs</a> is a place to find collaborators and build projects. We recently launched GitHub integrations. It’s easier than ever to find collaborators for your open source projects. And if you are looking for some people to start a project with, FindCollabs we have topic rooms that allow you to find other people who are interested in a particular technology, so that you can find people who are curious about React, or cryptocurrencies, or Kubernetes, or whatever you want to build with.</li> <li><a href="http://podsheets.com" rel="nofollow" target="_blank">Podsheets</a> is an open source podcast hosting platform that we recently launched. We are building Podsheets with the learnings from Software Engineering Daily, and our goal is to be the best place to host and monetize your podcast. If you have been thinking about starting a podcast, check out <a href="http://podsheets.com" rel="nofollow" target="_blank">podsheets.com</a>. </li> <li><a href="https://itunes.apple.com/us/app/software-engineering-daily/id1253734426?mt=8" rel="nofollow" target="_blank">New SEDaily app for iOS</a> and for <a href="https://play.google.com/store/apps/details?id=com.koalatea.sedaily" rel="nofollow" target="_blank">Android</a>. It includes all 1000 of our old episodes, as well as related links, greatest hits, and topics. You can comment on episodes and have discussions with other members of the community. I’ll be commenting on each episode, so if you hear an episode that you have some commentary on, jump onto the app, or on <a href="https://www.softwaredaily.com/" rel="nofollow" target="_blank">SoftwareDaily.com</a> to share your thoughts. And you can become a paid subscriber for ad free episodes at <a href="https://softwareengineeringdaily.com/subscribe" rel="nofollow" target="_blank">softwareengineeringdaily.com/subscribe</a>. <a href="http://altalogy.com" rel="nofollow" target="_blank">Altalogy</a> is the company who has been developing much of the software for the newest app, and if you are looking for a company to help you with your mobile and web development, I recommend checking them out.  </li> </ul> The post <a href="https://softwareengineeringdaily.com/2019/08/07/people-ai-machine-learning-for-sales-with-andrey-akselrod/" rel="nofollow" target="_blank">People.ai: Machine Learning for Sales with Andrey Akselrod</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

People.ai: Machine Learning for Sales with Andrey Akselrod

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