Dale Markowitz's guest appearances

Dale Markowitz

Google Cloud Platform Podcast

Platforms Windows, Linux, Apple all the Tech Info you can Handle on the Various Platforms

Dave Elliott

Andrew Walker

Peter Norvig

Eric Anderson

John Bohannon

Manuel Lima

Margaret Mitchell

Jessie Frazelle

Joanna Smith

Juliet Hougland

Michelle Casbon

Jesse Houston

Mark Chmarny

Ville Aikas

Sherol Chen

OwlTail

Craig Wiley

Jenny Brown co-hosts with Mark Mirchandani this week for a great conversation about the ML lifecycle with our guests Craig Wiley and Dale Markowitz. Using a real-life example of bus cameras detecting potholes, Dale and Craig walk us through the steps of designing, building, implementing, and improving on a piece of machine learning software. The first step, Craig tells us, is to identify the data collected and determine its viability in an ML model. He describes how to get the best data for your project and how to keep the data, code, and libraries consistent to allow better analysis by your ML models. He talks about the importance of a Feature Store to aid in data consistency. Craig explains how machine learning pipelines like TensorFlow are great tools to improve consistency in the ML environment as well, making it easier to improve your model and even to build new ones using the same data. Keeping this consistency from data scientist analyzation to ML developer to model deployment means a more efficient process and product. Evaluating models after production is an important step in the lifecycle as well to ensure accuracy, validity, and performance of the model. Craig gives us some examples and tips on monitoring models after they’ve been deployed. We talk about the challenges of scaling ML projects and Craig offers advice for developers and companies looking to build ML projects. Dale Markowitz Dale Markowitz is an Applied AI Engineer for ML on Google Cloud. Before that, she was a software engineer in Google Research and an engineer at the online dating site OkCupid. Craig Wiley Craig is the Director of Product for Google Cloud’s AI Platform. Previous to Google, Craig spent nine years at Amazon, as the General Manager of Amazon SageMaker, AWS’ machine learning platform as well as in Amazon’s 3rd Party Seller Business. Craig has a deep belief in democratizing the power of data, he pushes to improve the tooling for experienced users while seeking to simplifying it for the growing set of less experienced users. Outside of work he enjoys spending time with his family, eating delicious meals, and enthusiastically struggling through small home improvement projects. Cool things of the week  Introducing GKE Autopilot: a revolution in managed Kubernetes  blog At your service! With schedule-based autoscaling, VMs are at the ready  blog  Interview  Google Cloud AI and Machine Learning Products site GCP Podcast Episode 240: reCAPTCHA Enterprise with Kelly Anderson + Spring ML Potholes with Eric Clark  podcast Using machine learning to improve road maintenance  blog Key requirements for an MLOps foundation  blog TensorFLow site Kubeflow Pipelines site TensorBoard site How to dub a video with AI video Can AI make a good baking recipe? video Machine learning without code in the browser video  What’s something cool you’re working on? Jenny started a new podcast that reads interesting Google blog posts over at Google Cloud Reader. Our friend Dr. Anton Chuvakin started the Cloud Security Podcast by Google. Read more about it and listen  here. Follow the show and hosts on Twitter Cloud Security Podcast Anton and Tim And listen to Anton on the  GCP Podcast Episode 218: Chronicle Security with Dr. Anton Chuvakin and Ansh Patniak.

Jenny Brown co-hosts with <a href="https://twitter.com/markmirch" rel="nofollow" target="_blank">Mark Mirchandani</a> this week for a great conversation about the ML lifecycle with our guests Craig Wiley and Dale Markowitz. Using a real-life example of bus cameras detecting potholes, Dale and Craig walk us through the steps of designing, building, implementing, and improving on a piece of machine learning software. The first step, Craig tells us, is to identify the data collected and determine its viability in an ML model. He describes how to get the best data for your project and how to keep the data, code, and libraries consistent to allow better analysis by your ML models. He talks about the importance of a Feature Store to aid in data consistency. Craig explains how machine learning pipelines like TensorFlow are great tools to improve consistency in the ML environment as well, making it easier to improve your model and even to build new ones using the same data. Keeping this consistency from data scientist analyzation to ML developer to model deployment means a more efficient process and product. Evaluating models after production is an important step in the lifecycle as well to ensure accuracy, validity, and performance of the model. Craig gives us some examples and tips on monitoring models after they’ve been deployed. We talk about the challenges of scaling ML projects and Craig offers advice for developers and companies looking to build ML projects. Dale Markowitz Dale Markowitz is an Applied AI Engineer for ML on Google Cloud. Before that, she was a software engineer in Google Research and an engineer at the online dating site OkCupid. Craig Wiley Craig is the Director of Product for Google Cloud’s AI Platform. Previous to Google, Craig spent nine years at Amazon, as the General Manager of Amazon SageMaker, AWS’ machine learning platform as well as in Amazon’s 3rd Party Seller Business. Craig has a deep belief in democratizing the power of data, he pushes to improve the tooling for experienced users while seeking to simplifying it for the growing set of less experienced users. Outside of work he enjoys spending time with his family, eating delicious meals, and enthusiastically struggling through small home improvement projects. Cool things of the week <ul> <li>Introducing GKE Autopilot: a revolution in managed Kubernetes <a href="https://cloud.google.com/blog/products/containers-kubernetes/introducing-gke-autopilot" rel="nofollow" target="_blank"> blog</a></li> <li>At your service! With schedule-based autoscaling, VMs are at the ready <a href="https://cloud.google.com/blog/products/compute/introducing-schedule-based-autoscaling-for-compute-engine" rel="nofollow" target="_blank"> blog</a></li> </ul> Interview <ul> <li>Google Cloud AI and Machine Learning Products <a href="https://cloud.google.com/products/ai" rel="nofollow" target="_blank">site</a></li> <li>GCP Podcast Episode 240: reCAPTCHA Enterprise with Kelly Anderson + Spring ML Potholes with Eric Clark <a href="https://www.gcppodcast.com/post/episode-240-recaptcha-enterprise-kelly-anderson-springml-potholes-eric-clark/" rel="nofollow" target="_blank"> podcast</a></li> <li>Using machine learning to improve road maintenance <a href="https://cloud.google.com/blog/products/ai-machine-learning/video-intelligence-machine-learning-improves-pothole-detection" rel="nofollow" target="_blank"> blog</a></li> <li>Key requirements for an MLOps foundation <a href="https://cloud.google.com/blog/products/ai-machine-learning/key-requirements-for-an-mlops-foundation" rel="nofollow" target="_blank"> blog</a></li> <li>TensorFLow <a href="https://www.tensorflow.org" rel="nofollow" target="_blank">site</a></li> <li>Kubeflow Pipelines <a href="https://www.kubeflow.org/docs/pipelines/" rel="nofollow" target="_blank">site</a></li> <li>TensorBoard <a href="https://www.tensorflow.org/tensorboard" rel="nofollow" target="_blank">site</a></li> <li>How to dub a video with AI <a href="https://www.youtube.com/watch?v=T2TAAHmNBnE" rel="nofollow" target="_blank">video</a></li> <li>Can AI make a good baking recipe? <a href="https://www.youtube.com/watch?v=7sn8df97-JU" rel="nofollow" target="_blank">video</a></li> <li>Machine learning without code in the browser <a href="https://www.youtube.com/watch?v=i9tjzr1KME0" rel="nofollow" target="_blank">video</a></li> </ul> What’s something cool you’re working on? Jenny started a new podcast that reads interesting Google blog posts over at <a href="https://feeds.transistor.fm/google-cloud-reader" rel="nofollow" target="_blank">Google Cloud Reader</a>. Our friend Dr. Anton Chuvakin started the Cloud Security Podcast by Google. Read more about it and listen <a href="https://cloud.google.com/blog/products/identity-security/introducing-google-clouds-new-cloud-security-podcast" rel="nofollow" target="_blank"> here</a>. Follow the show and hosts on Twitter <a href="https://twitter.com/CloudSecPodcast" rel="nofollow" target="_blank">Cloud Security Podcast</a> <a href="https://twitter.com/anton_chuvakin" rel="nofollow" target="_blank">Anton</a> and <a href="https://twitter.com/_TimPeacock" rel="nofollow" target="_blank">Tim</a> And listen to Anton on the <a href="https://www.gcppodcast.com/post/episode-218-chronicle-security-with-dr-anton-chuvakin-and-ansh-patniak/" rel="nofollow" target="_blank"> GCP Podcast Episode 218: Chronicle Security with Dr. Anton Chuvakin and Ansh Patniak</a>.

ML Lifecycle with Dale Markowitz and Craig Wiley

Gabi Ferrara joins Mark Mirchandani today for an in-depth interview with Dale Markowitz about machine learning in the healthcare and medical fields. Dale talks about the coolest ways ML is transforming the healthcare field with advances in imaging and more accurate diagnoses of cancers. Later, Dale talks about how the cloud is used in healthcare to make data collection and sharing more efficient. The Google For Healthcare API, for example, makes working with common medical data types such as FHIR easier and more consistent. It helps with things like anonymizing of data and works with BigQuery for data analyzation. When data is collected and stored in the right format, it can be used to track healing progress, make health predictions, and more. Dale Markowitz Dale Markowitz is an Applied AI Engineer and Developer Advocate at Google. Cool things of the week  Google Game Developer Summit on Youtube  videos Simplifying Google Drive’s folder structure and sharing models  blog PostgreSQL 12 is in Beta on Google Cloud docs  New 96-core machine types for MySQL, PostgreSQL and SQL Server    Interview  Google’s lung cancer detection AI outperforms 6 human radiologists  article BigQuery site Cloud Healthcare API site Google FHIR docs Google Games Dev Summit Playlist  videos Building Contact Center AI Solutions with Quantiphi - Stack Chat video Verily site DeepMind site AlphaFold: Improved protein structure prediction using potentials from deep learning  research Computational predictions of protein structures associated with COVID-19  research How Machine Learning is Transforming Healthcare at Google and Beyond  blog How to develop machine learning models for healthcare article  Question of the week Where do I get started debugging performance for my MySQL database?  Diagnose and  Slow-Query Log  Where can you find us next? Gabi will be working Office Hours. Mark will be making more videos like KubeFlow 101 Series and  Stack Chat.

<a href="https://twitter.com/gabidavila" rel="nofollow" target="_blank">Gabi Ferrara</a> joins <a href="https://twitter.com/markmirch" rel="nofollow" target="_blank">Mark Mirchandani</a> today for an in-depth interview with <a href="https://twitter.com/dalequark" rel="nofollow" target="_blank">Dale Markowitz</a> about machine learning in the healthcare and medical fields. Dale talks about the coolest ways ML is transforming the healthcare field with advances in imaging and more accurate diagnoses of cancers. Later, Dale talks about how the cloud is used in healthcare to make data collection and sharing more efficient. The Google For Healthcare API, for example, makes working with common medical data types such as FHIR easier and more consistent. It helps with things like anonymizing of data and works with BigQuery for data analyzation. When data is collected and stored in the right format, it can be used to track healing progress, make health predictions, and more. Dale Markowitz Dale Markowitz is an Applied AI Engineer and Developer Advocate at Google. Cool things of the week <ul> <li>Google Game Developer Summit on Youtube <a href="https://www.youtube.com/user/GoogleDevelopers/playlists?view=50&amp;sort=dd&amp;shelf_id=88" rel="nofollow" target="_blank"> videos</a></li> <li>Simplifying Google Drive’s folder structure and sharing models <a href="https://cloud.google.com/blog/products/g-suite/simplifying-google-drives-folder-structure-and-sharing-models" rel="nofollow" target="_blank"> blog</a></li> <li>PostgreSQL 12 is in Beta on Google Cloud <a href="https://cloud.google.com/sql/docs/release-notes" rel="nofollow" target="_blank">docs</a> <ul> <li>New 96-core machine types for MySQL, PostgreSQL and SQL Server</li> </ul> </li> </ul> Interview <ul> <li>Google’s lung cancer detection AI outperforms 6 human radiologists <a href="https://venturebeat.com/2019/05/20/googles-lung-cancer-detection-ai-outperforms-6-human-radiologists/" rel="nofollow" target="_blank"> article</a></li> <li>BigQuery <a href="https://cloud.google.com/bigquery" rel="nofollow" target="_blank">site</a></li> <li>Cloud Healthcare API <a href="https://cloud.google.com/healthcare" rel="nofollow" target="_blank">site</a></li> <li>Google FHIR <a href="https://cloud.google.com/healthcare/docs/concepts/fhir" rel="nofollow" target="_blank">docs</a></li> <li>Google Games Dev Summit Playlist <a href="https://www.youtube.com/playlist?list=PLIivdWyY5sqI4HG9PQc1mplzlAFPvBh_3" rel="nofollow" target="_blank"> videos</a></li> <li>Building Contact Center AI Solutions with Quantiphi - Stack Chat <a href="https://www.youtube.com/watch?v=cTZArDgbIWw&amp;t=4s" rel="nofollow" target="_blank">video</a></li> <li>Verily <a href="https://verily.com" rel="nofollow" target="_blank">site</a></li> <li>DeepMind <a href="https://deepmind.com" rel="nofollow" target="_blank">site</a></li> <li>AlphaFold: Improved protein structure prediction using potentials from deep learning <a href="https://deepmind.com/research/publications/AlphaFold-Improved-protein-structure-prediction-using-potentials-from-deep-learning" rel="nofollow" target="_blank"> research</a></li> <li>Computational predictions of protein structures associated with COVID-19 <a href="https://deepmind.com/research/open-source/computational-predictions-of-protein-structures-associated-with-COVID-19" rel="nofollow" target="_blank"> research</a></li> <li>How Machine Learning is Transforming Healthcare at Google and Beyond <a href="https://towardsdatascience.com/how-machine-learning-is-transforming-healthcare-at-google-and-beyond-d4f664b7e27c" rel="nofollow" target="_blank"> blog</a></li> <li>How to develop machine learning models for healthcare <a href="https://www.nature.com/articles/s41563-019-0345-0" rel="nofollow" target="_blank">article</a></li> </ul> Question of the week Where do I get started debugging performance for my MySQL database? <ul> <li><a href="https://cloud.google.com/sql/docs/mysql/diagnose-issues" rel="nofollow" target="_blank">Diagnose</a> and <a href="https://cloud.google.com/community/tutorials/stackdriver-monitor-slow-query-mysql" rel="nofollow" target="_blank"> Slow-Query Log</a></li> </ul> Where can you find us next? Gabi will be working <a href="https://gabi.tips/office-hours" rel="nofollow" target="_blank">Office Hours</a>. Mark will be making more videos like <a href="https://www.youtube.com/watch?v=cTZArDgbIWw&amp;t=4s" rel="nofollow" target="_blank">KubeFlow 101 Series</a> and <a href="https://www.youtube.com/watch?v=Bu2uEX2nB9A&amp;list=PLIivdWyY5sqJvwGd0PTzSx1j0cePX0INl" rel="nofollow" target="_blank"> Stack Chat</a>.

AI in Healthcare with Dale Markowitz

On the podcast this week, we have a great interview with Google Developer Advocate, Dale Markowitz. Aja Hammerly and Jon Foust are your hosts, as we talk about machine learning, its best use cases, and how developers can break into machine learning and data science. Dale talks about natural language processing as well, explaining that it’s basically the intersection of machine learning and text processing. It can be used for anything from aggregating and sorting Twitter posts about your company to sentiment analysis. For developers looking to enter the machine learning space, Dale suggests starting with non life-threatening applications, such as labeling pictures. Next, consider the possible mistakes the application can make ahead of time to help mitigate issues. To help prevent the introduction of bias into the model, Dale suggests introducing it to as many different types of project-appropriate data sets as possible. It’s also important to continually monitor your model. Later in the show, we talk Google shop, learning about all the new features in Google Translate and AutoML. Dale Markowitz Dale Markowitz is an Applied AI Engineer and Developer Advocate for ML on Google Cloud. Before that she was a software engineer in Google Research and an engineer at the online dating site OkCupid. Cool things of the week  Build a dev workflow with Cloud Code on a Pixelbook  blog Feminism &amp; Agile  blog New homepage and improved collaboration features for AI Hub  blog  Interview  TensorFlow site Natural Language API site AutoML Natural Language  site Content Classification site Sentiment Analysis  site Analyzing Entities  site Translation API site AutoML Translate site Google Translate Glossary Documentation docs Google News Lab site AI Platform’s Data Labeling Service docs  Question of the week How many different ways can you run a container on GCP?  GKE Cloud Run App Engine Flexible Environment Compute Engine VM as a computer  Where can you find us next? Dale will be at DevFest Minneapolis, DevFest Madison, and London NEXT. Jon will be at the internal Google Game Summit and visiting Montreal. Aja will be holding down the fort at home. Sound Effect Attribution  “Mystery Peak2” by FoolBoyMedia of Freesound.org “Collect Point 00” by LittleRobotSoundFactory of Freesound.org “Cinematic Piano” by Ellary of Freesound.org 

On the podcast this week, we have a great interview with Google Developer Advocate, <a href="https://twitter.com/dalequark" rel="nofollow" target="_blank">Dale Markowitz</a>. <a href="https://twitter.com/the_thagomizer" rel="nofollow" target="_blank">Aja Hammerly</a> and <a href="https://twitter.com/syntxerror1" rel="nofollow" target="_blank">Jon Foust</a> are your hosts, as we talk about machine learning, its best use cases, and how developers can break into machine learning and data science. Dale talks about natural language processing as well, explaining that it’s basically the intersection of machine learning and text processing. It can be used for anything from aggregating and sorting Twitter posts about your company to sentiment analysis. For developers looking to enter the machine learning space, Dale suggests starting with non life-threatening applications, such as labeling pictures. Next, consider the possible mistakes the application can make ahead of time to help mitigate issues. To help prevent the introduction of bias into the model, Dale suggests introducing it to as many different types of project-appropriate data sets as possible. It’s also important to continually monitor your model. Later in the show, we talk Google shop, learning about all the new features in Google Translate and AutoML. Dale Markowitz <a href="https://twitter.com/dalequark" rel="nofollow" target="_blank">Dale Markowitz</a> is an Applied AI Engineer and Developer Advocate for ML on Google Cloud. Before that she was a software engineer in Google Research and an engineer at the online dating site OkCupid. Cool things of the week <ul> <li>Build a dev workflow with Cloud Code on a Pixelbook <a href="https://cloud.google.com/blog/products/application-development/build-a-dev-workflow-with-cloud-code-on-a-pixelbook" rel="nofollow" target="_blank"> blog</a></li> <li>Feminism &amp; Agile <a href="https://medium.com/@Hanna.Thomas/why-dont-we-just-call-agile-what-it-is-feminist-8bdd9193edba" rel="nofollow" target="_blank"> blog</a></li> <li>New homepage and improved collaboration features for AI Hub <a href="https://cloud.google.com/blog/products/ai-machine-learning/new-homepage-and-improved-collaboration-features-for-ai-hub" rel="nofollow" target="_blank"> blog</a></li> </ul> Interview <ul> <li>TensorFlow <a href="https://www.tensorflow.org" rel="nofollow" target="_blank">site</a></li> <li>Natural Language API <a href="https://cloud.google.com/natural-language/" rel="nofollow" target="_blank">site</a></li> <li>AutoML Natural Language <a href="https://cloud.google.com/natural-language/#how-automl-natural-language-works" rel="nofollow" target="_blank"> site</a></li> <li>Content Classification <a href="https://cloud.google.com/natural-language/docs/classifying-text" rel="nofollow" target="_blank">site</a></li> <li>Sentiment Analysis <a href="https://cloud.google.com/natural-language/docs/analyzing-sentiment" rel="nofollow" target="_blank"> site</a></li> <li>Analyzing Entities <a href="https://cloud.google.com/natural-language/docs/analyzing-entities" rel="nofollow" target="_blank"> site</a></li> <li>Translation API <a href="https://cloud.google.com/translate/" rel="nofollow" target="_blank">site</a></li> <li>AutoML Translate <a href="https://cloud.google.com/translate/#automl-translation" rel="nofollow" target="_blank">site</a></li> <li>Google Translate Glossary Documentation <a href="https://cloud.google.com/translate/docs/glossary" rel="nofollow" target="_blank">docs</a></li> <li>Google News Lab <a href="https://newsinitiative.withgoogle.com/google-news-lab" rel="nofollow" target="_blank">site</a></li> <li>AI Platform’s Data Labeling Service <a href="https://cloud.google.com/data-labeling/docs/" rel="nofollow" target="_blank">docs</a></li> </ul> Question of the week How many different ways can you run a container on GCP? <ul> <li><a href="https://cloud.google.com/kubernetes-engine/" rel="nofollow" target="_blank">GKE</a></li> <li><a href="https://cloud.google.com/run/" rel="nofollow" target="_blank">Cloud Run</a></li> <li><a href="https://cloud.google.com/appengine/docs/flexible/" rel="nofollow" target="_blank">App Engine Flexible Environment</a></li> <li><a href="https://cloud.google.com/compute/" rel="nofollow" target="_blank">Compute Engine</a></li> <li>VM as a computer</li> </ul> Where can you find us next? Dale will be at <a href="https://devfest.mn" rel="nofollow" target="_blank">DevFest Minneapolis</a>, <a href="https://devfestwi.com" rel="nofollow" target="_blank">DevFest Madison</a>, and <a href="https://cloud.withgoogle.com/next/uk" rel="nofollow" target="_blank">London NEXT</a>. Jon will be at the internal Google Game Summit and visiting Montreal. Aja will be holding down the fort at home. Sound Effect Attribution <ul> <li>“Mystery Peak2” by FoolBoyMedia of <a href="https://freesound.org" rel="nofollow" target="_blank">Freesound.org</a></li> <li>“Collect Point 00” by LittleRobotSoundFactory of <a href="https://freesound.org" rel="nofollow" target="_blank">Freesound.org</a></li> <li>“Cinematic Piano” by Ellary of <a href="https://freesound.org" rel="nofollow" target="_blank">Freesound.org</a></li> </ul>

ML with Dale Markowitz