Jed Dougherty's guest appearances

Jed Dougherty

Google Cloud Platform Podcast

HumAIn Podcast - Artificial Intelligence, Data Science, Developer Tools, and Technical Education

Columbia Data Science Society Podcast

Nikita Shamgunov

Chris Butler

Mike Robbins

Lorna Davis

Jake Porway

Stephen Miller

Robert Scoble

John Spence

Babak Hodjat

Vasco Pedro

Lex Sokolin

Jordan Tigani

Magnus Carlsen

Thor Ernstsson

Alex Beard

OwlTail

Dan Darnell

On the show this week, Carter Morgan and Anu Srivastava talk about AI and ML data analytics with Dataiku VP of Platform Strategy, Jed Dougherty, and Head of Product Marketing, Dan Darnell. Dataiku is an AI platform targeted for business team collaboration. The low and no code environments make it easy for developers and not so tech savvy employees to work together on analytics projects. It strives for everyday AI, making these normally highly technical data processes more accessible. Our guests detail the tools Dataiku provides customers, including ML Ops features for efficient models. Dataiku’s managed offering allows businesses to concentrate on the model while Dataiku takes care of things like the deployment processes behind the scenes. We hear about the partnership between Dataiku and Google Cloud and Dataiku’s integration with AlloyDB. Through a real example, our guests run us through the use of these two tools together. Jed talks about why Google Cloud works so well with Dataiku, especially for businesses looking for cutting edge technology. Jed Dougherty Jed is the VP of Platform Strategy at Dataiku. In this role he acts as a strategic technical advisor to Dataiku customers and prospects. He also works tightly with Engineering and Product stakeholders in order to ensure that all technical platform requests are properly followed, scoped and implemented. Dan Darnell Dan has over 20 years of experience in the analytics industry at established software companies, hyper-growth technology companies, and small technology start-ups. As the Head of Product Marketing at Dataiku, he owns positioning, evangelism, and content creation for product offerings and education on products for customers and partners. Cool things of the week  Google Cloud supercharges NLP with large language models blog Practicing the principle of least privilege with Cloud Build and Artifact Registry blog  Interview  Dataiku site Dataiku YouTube videos BigQuery site Kubernetes site GKE site AlloyDB for PostgreSQL site Accelerate AI Adoption: 3 Steps to Deploy Dataiku for Google Cloud Platform blog Implementing Dataiku with BigQuery docs GCP Podcast Episode 238: ASML with Arnaud Hubaux podcast GCP Podcast Episode 229: Lucidworks with Radu Miclaus podcast  What’s something cool you’re working on? Anu is working on interesting speech use cases and Google’s Speech to Text. Join in with this tutorial! Carter is working on getting organized and working on something super cool! Hosts Carter Morgan and Anu Srivastava

On the show this week, <a href="https://twitter.com/carterthecomic" rel="noopener nofollow" target="_blank">Carter Morgan</a> and <a href="https://twitter.com/asrivas_dev" rel="noopener nofollow" target="_blank">Anu Srivastava</a> talk about AI and ML data analytics with Dataiku VP of Platform Strategy, Jed Dougherty, and Head of Product Marketing, Dan Darnell. Dataiku is an AI platform targeted for business team collaboration. The low and no code environments make it easy for developers and not so tech savvy employees to work together on analytics projects. It strives for everyday AI, making these normally highly technical data processes more accessible. Our guests detail the tools Dataiku provides customers, including ML Ops features for efficient models. Dataiku’s managed offering allows businesses to concentrate on the model while Dataiku takes care of things like the deployment processes behind the scenes. We hear about the partnership between Dataiku and Google Cloud and Dataiku’s integration with AlloyDB. Through a real example, our guests run us through the use of these two tools together. Jed talks about why Google Cloud works so well with Dataiku, especially for businesses looking for cutting edge technology. Jed Dougherty Jed is the VP of Platform Strategy at Dataiku. In this role he acts as a strategic technical advisor to Dataiku customers and prospects. He also works tightly with Engineering and Product stakeholders in order to ensure that all technical platform requests are properly followed, scoped and implemented. Dan Darnell Dan has over 20 years of experience in the analytics industry at established software companies, hyper-growth technology companies, and small technology start-ups. As the Head of Product Marketing at Dataiku, he owns positioning, evangelism, and content creation for product offerings and education on products for customers and partners. Cool things of the week <ul> <li>Google Cloud supercharges NLP with large language models <a href="https://cloud.google.com/blog/products/ai-machine-learning/google-cloud-supercharges-nlp-with-large-language-models" rel="noopener nofollow" target="_blank">blog</a></li> <li>Practicing the principle of least privilege with Cloud Build and Artifact Registry <a href="https://cloud.google.com/blog/topics/developers-practitioners/practicing-principle-least-privilege-cloud-build-and-artifact-registry" rel="noopener nofollow" target="_blank">blog</a></li> </ul> Interview <ul> <li>Dataiku <a href="https://www.dataiku.com/" rel="noopener nofollow" target="_blank">site</a></li> <li>Dataiku YouTube <a href="https://www.youtube.com/c/Dataiku" rel="noopener nofollow" target="_blank">videos</a></li> <li>BigQuery <a href="https://cloud.google.com/bigquery" rel="noopener nofollow" target="_blank">site</a></li> <li>Kubernetes <a href="https://kubernetes.io/" rel="noopener nofollow" target="_blank">site</a></li> <li>GKE <a href="https://cloud.google.com/kubernetes-engine" rel="noopener nofollow" target="_blank">site</a></li> <li>AlloyDB for PostgreSQL <a href="https://cloud.google.com/alloydb" rel="noopener nofollow" target="_blank">site</a></li> <li>Accelerate AI Adoption: 3 Steps to Deploy Dataiku for Google Cloud Platform <a href="https://blog.dataiku.com/deploy-dataiku-for-google-cloud-platform" rel="noopener nofollow" target="_blank">blog</a></li> <li>Implementing Dataiku with BigQuery <a href="https://doc.dataiku.com/dss/latest/connecting/sql/bigquery.html" rel="noopener nofollow" target="_blank">docs</a></li> <li>GCP Podcast Episode 238: ASML with Arnaud Hubaux <a href="https://www.gcppodcast.com/post/episode-238-asml-with-arnaud-hubaux/" rel="noopener nofollow" target="_blank">podcast</a></li> <li>GCP Podcast Episode 229: Lucidworks with Radu Miclaus <a href="https://www.gcppodcast.com/post/episode-229-lucidworks-with-radu-miclaus/" rel="noopener nofollow" target="_blank">podcast</a></li> </ul> What’s something cool you’re working on? Anu is working on interesting speech use cases and Google’s Speech to Text. Join in with this <a href="https://cloud-dot-devsite-v2-prod.appspot.com/walkthroughs/speech-to-text/speech-to-text-java" rel="noopener nofollow" target="_blank">tutorial</a>! Carter is working on getting organized and working on something super cool! Hosts Carter Morgan and Anu Srivastava

ML/AI Data Science for Data Analytics with Jed Dougherty and Dan Darnell

Jed Dougherty is VP of Field Engineering at Dataiku. He specializes in helping companies construct enterprise-grade data platforms and has helped teams around the world build successful production infrastructures across the various clouds. He holds a master’s degree from the QMSS Program at Columbia University and Degrees in Mathematics and Political Science from Arizona State.  Additional Links: Columbia Statistics Class Blog Post: https://statmodeling.stat.columbia.edu/2013/11/01/data-science/

Jed Dougherty is VP of Field Engineering at Dataiku. He specializes in helping companies construct enterprise-grade data platforms and has helped teams around the world build successful production infrastructures across the various clouds. He holds a master’s degree from the QMSS Program at Columbia University and Degrees in Mathematics and Political Science from Arizona State. Additional Links: Columbia Statistics Class Blog Post: <a href="https://statmodeling.stat.columbia.edu/2013/11/01/data-science/" rel="nofollow" target="_blank">https://statmodeling.stat.columbia.edu/2013/11/01/data-science/</a>

Episode 4: Jed Dougherty

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSJed Dougherty is the VP of Field Engineering at Dataiku. He specializes in helping companies construct enterprise-grade data platforms and has helped teams around the world build successful production infrastructures across the various clouds and on-prem. He holds a master’s degree from the QMSS Program at Columbia University and Degrees in Mathematics and Political Science from Arizona State.Episode Links:  Jed Doughtery’s LinkedIn: https://www.linkedin.com/in/jediv/ Jed Doughtery’s Twitter:  https://twitter.com/dataiku Jed Doughtery’s Website: https://www.dataiku.com/ Podcast Details: Podcast website: https://www.humainpodcast.comApple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpSRSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1ragYouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videosSupport and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators– Twitter:  https://twitter.com/dyakobovitch– Instagram: https://www.instagram.com/humainpodcast/– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/– Facebook: https://www.facebook.com/HumainPodcast/– HumAIn Website Articles: https://www.humainpodcast.com/blog/Outline: Here’s the timestamps for the episode: (00:00) – Introduction.(02:03) – Making AI and Data Science relatively easy to use instead of limiting it to a few smart dudes encourages a more evenly distribution of the power that comes from it.(03:27) – Google and Amazon want to keep control of the actual algorithms. (04:37) – No big company in America, except Google, Amazon, Facebook and Netflix is able to hit the median income for their data scientists these giants have, which means they have a different pool of talent to pull from.(07:22) – Universal Basic Income as a solution for a feasible future of jobs being replaced as a result of automation.(12:27) – Empathy mapping to design AI systems to be diverse, inclusive and trained for multiple scenarios. AI has been about prediction and not an explanation of these predictions. Models should be more explainable than accurate.(13:02) – Some of the new product lines for the explainability of an AI built by Google and Amazon .(17:08) – Pushing the power back to the user immediately to empower them to have decisions driven by AI. (11:53) – AI governance and ethical decision-making. If you don't have people connected to the things you're trying to predict, it's easy to miss a trend to assume that you have complete data when you do not.(20:29) – Fair AI systems: labels are generated by humans, which means they have all the failures and foibles of our current society. Pushing those into a model makes that model exactly as good as our current society or worse. (23:06) – We overestimated human enthusiasm for autonomous driving.(28:51) – Computer centralized systems are weak. If Google, Amazon and Facebok were one single company, they would have this complete idea of your life and be able to predict every moment of it and say, how much of a worthy individual you were to society.(32:35) – In NYC, people from all walks of life run into each other, touch each other accidentally. Nobody owns New York. You have the people who are gonna be affected by AI, the business knowledge and a growing tech base of folks who can implement technology. NYC could be the center of machine learning.(36:22) – Linux command line is running 99% of the servers in the world right now(38:32) – Knowledge to become tech-relevant: this is such a new industry that 10 years at school, you may have learned 5% of the industry.(43:03) – Humans and the machines. Things from an ethical perspective or a human perspective combined with technical knowledge.Advertising Inquiries: https://redcircle.com/brandsPrivacy &amp; Opt-Out: https://redcircle.com/privacy

[Audio] Podcast:<a href="https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS" rel="nofollow" target="_blank"> Play in new window</a> |<a href="https://www.listennotes.com/podcasts/humain-podcast-artificial-intelligence-data-fBRret2PTiU/" rel="nofollow" target="_blank"> Download</a>Subscribe:<a href="https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5yZWRjaXJjbGUuY29tLzk5MTEzZjI0LTJiZDEtNDMzMi04Y2QwLTMyZTA1NTZjOGJjOQ" rel="nofollow" target="_blank"> Google Podcasts</a> |<a href="https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS" rel="nofollow" target="_blank"> Spotify</a> |<a href="https://www.stitcher.com/show/humain" rel="nofollow" target="_blank"> Stitcher</a> | <a href="https://tunein.com/podcasts/Technology-Podcasts/HumAIn-p1224678/" rel="nofollow" target="_blank">TuneIn</a> | <a href="https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9" rel="nofollow" target="_blank">RSS</a>Jed Dougherty is the VP of Field Engineering at Dataiku. He specializes in helping companies construct enterprise-grade data platforms and has helped teams around the world build successful production infrastructures across the various clouds and on-prem. He holds a master’s degree from the QMSS Program at Columbia University and Degrees in Mathematics and Political Science from Arizona State.Episode Links:  Jed Doughtery’s LinkedIn:<a href="https://www.linkedin.com/in/iamjdeleon/" rel="nofollow" target="_blank"> </a><a href="https://www.linkedin.com/in/jediv/" rel="nofollow" target="_blank">https://www.linkedin.com/in/jediv/</a> Jed Doughtery’s Twitter:  <a href="https://twitter.com/dataiku" rel="nofollow" target="_blank">https://twitter.com/dataiku</a> Jed Doughtery’s Website: <a href="https://www.dataiku.com/" rel="nofollow" target="_blank">https://www.dataiku.com/</a> Podcast Details: Podcast website: <a href="https://www.humainpodcast.com/" rel="nofollow" target="_blank">https://www.humainpodcast.com</a>Apple Podcasts: <a href="https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009" rel="nofollow" target="_blank"> https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009</a>Spotify: <a href="https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS" rel="nofollow" target="_blank"> https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS</a>RSS: <a href="https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9" rel="nofollow" target="_blank">https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9</a>YouTube Full Episodes: <a href="https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag" rel="nofollow" target="_blank">https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag</a>YouTube Clips: <a href="https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos" rel="nofollow" target="_blank"> https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos</a>Support and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: <a href="https://www.patreon.com/humain/creators" rel="nofollow" target="_blank">https://www.patreon.com/humain/creators</a>– Twitter: <a href="https://twitter.com/dyakobovitch" rel="nofollow" target="_blank"> https://twitter.com/dyakobovitch</a>– Instagram:<a href="https://www.instagram.com/humainpodcast/" rel="nofollow" target="_blank"> https://www.instagram.com/humainpodcast/</a>– LinkedIn: <a href="https://www.linkedin.com/in/davidyakobovitch/" rel="nofollow" target="_blank">https://www.linkedin.com/in/davidyakobovitch/</a>– Facebook: <a href="https://www.facebook.com/HumainPodcast/" rel="nofollow" target="_blank">https://www.facebook.com/HumainPodcast/</a>– HumAIn Website Articles:<a href="https://www.humainpodcast.com/blog/" rel="nofollow" target="_blank"> https://www.humainpodcast.com/blog/</a>Outline: Here’s the timestamps for the episode: (00:00) – Introduction.(02:03) – Making AI and Data Science relatively easy to use instead of limiting it to a few smart dudes encourages a more evenly distribution of the power that comes from it.(03:27) – Google and Amazon want to keep control of the actual algorithms. (04:37) – No big company in America, except Google, Amazon, Facebook and Netflix is able to hit the median income for their data scientists these giants have, which means they have a different pool of talent to pull from.(07:22) – Universal Basic Income as a solution for a feasible future of jobs being replaced as a result of automation.(12:27) – Empathy mapping to design AI systems to be diverse, inclusive and trained for multiple scenarios. AI has been about prediction and not an explanation of these predictions. Models should be more explainable than accurate.(13:02) – Some of the new product lines for the explainability of an AI built by Google and Amazon .(17:08) – Pushing the power back to the user immediately to empower them to have decisions driven by AI. (11:53) – AI governance and ethical decision-making. If you don't have people connected to the things you're trying to predict, it's easy to miss a trend to assume that you have complete data when you do not.(20:29) – Fair AI systems: labels are generated by humans, which means they have all the failures and foibles of our current society. Pushing those into a model makes that model exactly as good as our current society or worse. (23:06) – We overestimated human enthusiasm for autonomous driving.(28:51) – Computer centralized systems are weak. If Google, Amazon and Facebok were one single company, they would have this complete idea of your life and be able to predict every moment of it and say, how much of a worthy individual you were to society.(32:35) – In NYC, people from all walks of life run into each other, touch each other accidentally. Nobody owns New York. You have the people who are gonna be affected by AI, the business knowledge and a growing tech base of folks who can implement technology. NYC could be the center of machine learning.(36:22) – Linux command line is running 99% of the servers in the world right now(38:32) – Knowledge to become tech-relevant: this is such a new industry that 10 years at school, you may have learned 5% of the industry.(43:03) – Humans and the machines. Things from an ethical perspective or a human perspective combined with technical knowledge.Advertising Inquiries: <a href="https://redcircle.com/brands" rel="nofollow" target="_blank">https://redcircle.com/brands</a> Privacy &amp; Opt-Out: <a href="https://redcircle.com/privacy" rel="nofollow" target="_blank">https://redcircle.com/privacy</a>

Exploring the Future of AI with Jed Dougherty