Steve Hsu wears many hats, but most recently he is co-founder of SuperFocus, a startup building LLM-backed knowledge co-pilots for enterprises.Subscribe to the Gradient Flow Newsletter:  https://gradientflow.substack.com/Subscribe: Apple • Spotify • Overcast • Google • AntennaPod • Podcast Addict • Amazon •  RSS.Detailed show notes can be found on The Data Exchange web site.

<a href="https://www.linkedin.com/in/stevehsu/" rel="nofollow" target="_blank">Steve Hsu</a> wears many hats, but most recently he is co-founder of <a href="https://superfocus.ai/" rel="nofollow" target="_blank">SuperFocus</a>, a startup building LLM-backed knowledge co-pilots for enterprises. Subscribe to the Gradient Flow Newsletter:  <a href="https://gradientflow.substack.com/" rel="nofollow" target="_blank">https://gradientflow.substack.com/</a> Subscribe: <a href="https://podcasts.apple.com/us/podcast/the-data-exchange-with-ben-lorica/id1487704458" rel="nofollow" target="_blank">Apple</a> • <a href="https://open.spotify.com/show/65oFWYoomwT8mLcdGkUGd0" rel="nofollow" target="_blank">Spotify</a> • <a href="https://overcast.fm/itunes1487704458" rel="nofollow" target="_blank">Overcast</a> • <a href="https://podcasts.google.com/?feed=aHR0cHM6Ly9mZWVkcy5idXp6c3Byb3V0LmNvbS82ODI0MzMucnNz" rel="nofollow" target="_blank">Google</a> • <a href="https://antennapod.org/deeplink/subscribe/?url=feeds.buzzsprout.com/682433.rss" rel="nofollow" target="_blank">AntennaPod</a> • <a href="https://podcastaddict.com/podcast/2476584" rel="nofollow" target="_blank">Podcast Addict</a> • <a href="https://music.amazon.com/podcasts/18e76845-60a8-4263-9c41-a7bdfe9d14a9/the-data-exchange-with-ben-lorica" rel="nofollow" target="_blank">Amazon</a> •  <a href="https://feeds.buzzsprout.com/682433.rss" rel="nofollow" target="_blank">RSS</a>. Detailed show notes can be found <a href="https://thedataexchange.media/using-llms-to-build-ai-co-pilots-for-knowledge-workers/" rel="nofollow" target="_blank">on The Data Exchange web site</a>.

Using LLMs to Build AI Co-pilots for Knowledge Workers

Brian Raymond is the founder of Unstructured, a startup building open source data pre-processing and ingestion tools specifically for Large Language Models (LLMs). Subscribe to the Gradient Flow Newsletter:  https://gradientflow.substack.com/Subscribe: Apple • Spotify • Overcast • Google • AntennaPod • Podcast Addict • Amazon •  RSS.Detailed show notes can be found on The Data Exchange web site.

<a href="https://www.linkedin.com/in/brian-s-raymond/" rel="nofollow" target="_blank">Brian Raymond</a> is the founder of <a href="https://www.unstructured.io/" rel="nofollow" target="_blank">Unstructured</a>, a startup building open source data pre-processing and ingestion tools specifically for Large Language Models (LLMs).  Subscribe to the Gradient Flow Newsletter:  <a href="https://gradientflow.substack.com/" rel="nofollow" target="_blank">https://gradientflow.substack.com/</a> Subscribe: <a href="https://podcasts.apple.com/us/podcast/the-data-exchange-with-ben-lorica/id1487704458" rel="nofollow" target="_blank">Apple</a> • <a href="https://open.spotify.com/show/65oFWYoomwT8mLcdGkUGd0" rel="nofollow" target="_blank">Spotify</a> • <a href="https://overcast.fm/itunes1487704458" rel="nofollow" target="_blank">Overcast</a> • <a href="https://podcasts.google.com/?feed=aHR0cHM6Ly9mZWVkcy5idXp6c3Byb3V0LmNvbS82ODI0MzMucnNz" rel="nofollow" target="_blank">Google</a> • <a href="https://antennapod.org/deeplink/subscribe/?url=feeds.buzzsprout.com/682433.rss" rel="nofollow" target="_blank">AntennaPod</a> • <a href="https://podcastaddict.com/podcast/2476584" rel="nofollow" target="_blank">Podcast Addict</a> • <a href="https://music.amazon.com/podcasts/18e76845-60a8-4263-9c41-a7bdfe9d14a9/the-data-exchange-with-ben-lorica" rel="nofollow" target="_blank">Amazon</a> •  <a href="https://feeds.buzzsprout.com/682433.rss" rel="nofollow" target="_blank">RSS</a>. Detailed show notes can be found <a href="https://thedataexchange.media/etl-for-llms/" rel="nofollow" target="_blank">on The Data Exchange web site</a>.

ETL for LLMs

Emil Eifrem is co-founder and CEO of Neo4j, the leading graph database and graph data science software provider. We discussed a range of topics including: the current state of graph databases, graph data science and graph neural networks, vector databases, the interplay between LLMs, knowledge graphs, and graph databases.Subscribe to the Gradient Flow Newsletter:  https://gradientflow.substack.com/Subscribe: Apple • Spotify • Stitcher • Google • AntennaPod • Podcast Addict • Amazon •  RSS.Detailed show notes can be found on The Data Exchange web site.

<a href="https://www.linkedin.com/in/emileifrem/" rel="nofollow" target="_blank">Emil Eifrem</a> is co-founder and CEO of <a href="https://neo4j.com/" rel="nofollow" target="_blank">Neo4j</a>, the leading graph database and graph data science software provider. We discussed a range of topics including: the current state of graph databases, graph data science and graph neural networks, vector databases, the interplay between LLMs, knowledge graphs, and graph databases. Subscribe to the Gradient Flow Newsletter:  <a href="https://gradientflow.substack.com/" rel="nofollow" target="_blank">https://gradientflow.substack.com/</a> Subscribe: <a href="https://podcasts.apple.com/us/podcast/the-data-exchange-with-ben-lorica/id1487704458" rel="nofollow" target="_blank">Apple</a> • <a href="https://open.spotify.com/show/65oFWYoomwT8mLcdGkUGd0" rel="nofollow" target="_blank">Spotify</a> • <a href="https://www.stitcher.com/show/the-data-exchange" rel="nofollow" target="_blank">Stitcher</a> • <a href="https://podcasts.google.com/?feed=aHR0cHM6Ly9mZWVkcy5idXp6c3Byb3V0LmNvbS82ODI0MzMucnNz" rel="nofollow" target="_blank">Google</a> • <a href="https://antennapod.org/deeplink/subscribe/?url=feeds.buzzsprout.com/682433.rss" rel="nofollow" target="_blank">AntennaPod</a> • <a href="https://podcastaddict.com/podcast/2476584" rel="nofollow" target="_blank">Podcast Addict</a> • <a href="https://music.amazon.com/podcasts/18e76845-60a8-4263-9c41-a7bdfe9d14a9/the-data-exchange-with-ben-lorica" rel="nofollow" target="_blank">Amazon</a> •  <a href="https://feeds.buzzsprout.com/682433.rss" rel="nofollow" target="_blank">RSS</a>. Detailed show notes can be found <a href="https://thedataexchange.media/the-future-of-graph-databases/" rel="nofollow" target="_blank">on The Data Exchange web site</a>.

The Future of Graph Databases

David Talby is the CTO and Founder of John Snow Labs, the company behind two popular open source projects: Spark NLP and LangTest.  In this episode we focus on LangTest, an open-source Python library designed to help developers deliver safe and effective Natural Language Processing (NLP) models. [Note: After we recorded this episode, NLTest was renamed to LangTest.]Subscribe to the Gradient Flow Newsletter:  https://gradientflow.substack.com/Subscribe: Apple • Spotify • Stitcher • Google • AntennaPod • Podcast Addict • Amazon •  RSS.Detailed show notes can be found on The Data Exchange web site.

<a href="https://www.linkedin.com/in/davidtalby/" rel="nofollow" target="_blank">David Talby</a> is the CTO and Founder of <a href="https://www.johnsnowlabs.com/" rel="nofollow" target="_blank">John Snow Labs</a>, the company behind two popular open source projects: Spark NLP and <a href="https://langtest.org/" rel="nofollow" target="_blank">LangTest</a>.  In this episode we focus on LangTest, an open-source Python library designed to help developers deliver safe and effective Natural Language Processing (NLP) models. [Note: After we recorded this episode, NLTest was renamed to LangTest.] Subscribe to the Gradient Flow Newsletter:  <a href="https://gradientflow.substack.com/" rel="nofollow" target="_blank">https://gradientflow.substack.com/</a> Subscribe: <a href="https://podcasts.apple.com/us/podcast/the-data-exchange-with-ben-lorica/id1487704458" rel="nofollow" target="_blank">Apple</a> • <a href="https://open.spotify.com/show/65oFWYoomwT8mLcdGkUGd0" rel="nofollow" target="_blank">Spotify</a> • <a href="https://www.stitcher.com/show/the-data-exchange" rel="nofollow" target="_blank">Stitcher</a> • <a href="https://podcasts.google.com/?feed=aHR0cHM6Ly9mZWVkcy5idXp6c3Byb3V0LmNvbS82ODI0MzMucnNz" rel="nofollow" target="_blank">Google</a> • <a href="https://antennapod.org/deeplink/subscribe/?url=feeds.buzzsprout.com/682433.rss" rel="nofollow" target="_blank">AntennaPod</a> • <a href="https://podcastaddict.com/podcast/2476584" rel="nofollow" target="_blank">Podcast Addict</a> • <a href="https://music.amazon.com/podcasts/18e76845-60a8-4263-9c41-a7bdfe9d14a9/the-data-exchange-with-ben-lorica" rel="nofollow" target="_blank">Amazon</a> •  <a href="https://feeds.buzzsprout.com/682433.rss" rel="nofollow" target="_blank">RSS</a>. Detailed show notes can be found <a href="https://thedataexchange.media/langtest/" rel="nofollow" target="_blank">on The Data Exchange web site</a>.

Delivering Safe and Effective LLM and NLP Applications

Jeff Jonas is Founder and CEO of Senzing, a startup focused on democratizing entity resolution – making this deceptively complicated task easy for programmers to use and deploy.Subscribe to the Gradient Flow Newsletter:  https://gradientflow.substack.com/Subscribe: Apple • Spotify • Stitcher • Google • AntennaPod • Podcast Addict • Amazon •  RSS.Detailed show notes can be found on The Data Exchange web site.

<a href="https://senzing.com/jeff-jonas/" rel="nofollow" target="_blank">Jeff Jonas</a> is Founder and CEO of <a href="https://senzing.com/" rel="nofollow" target="_blank">Senzing</a>, a startup focused on democratizing entity resolution – making this deceptively complicated task easy for programmers to use and deploy. Subscribe to the Gradient Flow Newsletter:  <a href="https://gradientflow.substack.com/" rel="nofollow" target="_blank">https://gradientflow.substack.com/</a> Subscribe: <a href="https://podcasts.apple.com/us/podcast/the-data-exchange-with-ben-lorica/id1487704458" rel="nofollow" target="_blank">Apple</a> • <a href="https://open.spotify.com/show/65oFWYoomwT8mLcdGkUGd0" rel="nofollow" target="_blank">Spotify</a> • <a href="https://www.stitcher.com/show/the-data-exchange" rel="nofollow" target="_blank">Stitcher</a> • <a href="https://podcasts.google.com/?feed=aHR0cHM6Ly9mZWVkcy5idXp6c3Byb3V0LmNvbS82ODI0MzMucnNz" rel="nofollow" target="_blank">Google</a> • <a href="https://antennapod.org/deeplink/subscribe/?url=feeds.buzzsprout.com/682433.rss" rel="nofollow" target="_blank">AntennaPod</a> • <a href="https://podcastaddict.com/podcast/2476584" rel="nofollow" target="_blank">Podcast Addict</a> • <a href="https://music.amazon.com/podcasts/18e76845-60a8-4263-9c41-a7bdfe9d14a9/the-data-exchange-with-ben-lorica" rel="nofollow" target="_blank">Amazon</a> •  <a href="https://feeds.buzzsprout.com/682433.rss" rel="nofollow" target="_blank">RSS</a>. Detailed show notes can be found <a href="https://thedataexchange.media/using-data-and-ai-to-democratize-entity-resolution-and-master-data-management/" rel="nofollow" target="_blank">on The Data Exchange web site</a>.

Using Data and AI to Democratize Entity Resolution and Master Data Management

Jerry Liu is CEO and co-founder of LlamaIndex, an open source project and startup that builds tools that enable teams to augment LLMs with their own private data. Subscribe to the Gradient Flow Newsletter:  https://gradientflow.substack.com/Subscribe: Apple • Spotify • Stitcher • Google • AntennaPod • Podcast Addict • Amazon •  RSS.Detailed show notes can be found on The Data Exchange web site.

<a href="https://www.linkedin.com/in/jerry-liu-64390071/" rel="nofollow" target="_blank">Jerry Liu</a> is CEO and co-founder of <a href="https://github.com/jerryjliu/llama_index" rel="nofollow" target="_blank">LlamaIndex</a>, an open source project and startup that builds tools that enable teams to augment LLMs with their own private data.  Subscribe to the Gradient Flow Newsletter:  <a href="https://gradientflow.substack.com/" rel="nofollow" target="_blank">https://gradientflow.substack.com/</a> Subscribe: <a href="https://podcasts.apple.com/us/podcast/the-data-exchange-with-ben-lorica/id1487704458" rel="nofollow" target="_blank">Apple</a> • <a href="https://open.spotify.com/show/65oFWYoomwT8mLcdGkUGd0" rel="nofollow" target="_blank">Spotify</a> • <a href="https://www.stitcher.com/show/the-data-exchange" rel="nofollow" target="_blank">Stitcher</a> • <a href="https://podcasts.google.com/?feed=aHR0cHM6Ly9mZWVkcy5idXp6c3Byb3V0LmNvbS82ODI0MzMucnNz" rel="nofollow" target="_blank">Google</a> • <a href="https://antennapod.org/deeplink/subscribe/?url=feeds.buzzsprout.com/682433.rss" rel="nofollow" target="_blank">AntennaPod</a> • <a href="https://podcastaddict.com/podcast/2476584" rel="nofollow" target="_blank">Podcast Addict</a> • <a href="https://music.amazon.com/podcasts/18e76845-60a8-4263-9c41-a7bdfe9d14a9/the-data-exchange-with-ben-lorica" rel="nofollow" target="_blank">Amazon</a> •  <a href="https://feeds.buzzsprout.com/682433.rss" rel="nofollow" target="_blank">RSS</a>. Detailed show notes can be found <a href="https://thedataexchange.media/an-open-source-data-framework-for-llms/ " rel="nofollow" target="_blank">on The Data Exchange web site</a>.

An Open Source Data Framework for LLMs

Tim Davis is the Co-Founder &amp; Chief Product Officer of Modular, a startup focused on building tools to help simplify AI infrastructure.Subscribe to the Gradient Flow Newsletter:  https://gradientflow.substack.com/Subscribe: Apple • Spotify • Stitcher • Google • AntennaPod • Podcast Addict • Amazon •  RSS.Detailed show notes can be found on The Data Exchange web site.

<a href="https://www.linkedin.com/in/timdavisau/" rel="nofollow" target="_blank">Tim Davis</a> is the Co-Founder &amp; Chief Product Officer of <a href="https://www.modular.com/" rel="nofollow" target="_blank">Modular</a>, a startup focused on building tools to help simplify AI infrastructure. Subscribe to the Gradient Flow Newsletter:  <a href="https://gradientflow.substack.com/" rel="nofollow" target="_blank">https://gradientflow.substack.com/</a> Subscribe: <a href="https://podcasts.apple.com/us/podcast/the-data-exchange-with-ben-lorica/id1487704458" rel="nofollow" target="_blank">Apple</a> • <a href="https://open.spotify.com/show/65oFWYoomwT8mLcdGkUGd0" rel="nofollow" target="_blank">Spotify</a> • <a href="https://www.stitcher.com/show/the-data-exchange" rel="nofollow" target="_blank">Stitcher</a> • <a href="https://podcasts.google.com/?feed=aHR0cHM6Ly9mZWVkcy5idXp6c3Byb3V0LmNvbS82ODI0MzMucnNz" rel="nofollow" target="_blank">Google</a> • <a href="https://antennapod.org/deeplink/subscribe/?url=feeds.buzzsprout.com/682433.rss" rel="nofollow" target="_blank">AntennaPod</a> • <a href="https://podcastaddict.com/podcast/2476584" rel="nofollow" target="_blank">Podcast Addict</a> • <a href="https://music.amazon.com/podcasts/18e76845-60a8-4263-9c41-a7bdfe9d14a9/the-data-exchange-with-ben-lorica" rel="nofollow" target="_blank">Amazon</a> •  <a href="https://feeds.buzzsprout.com/682433.rss" rel="nofollow" target="_blank">RSS</a>. Detailed show notes can be found <a href="https://thedataexchange.media/redefining-ai-infrastructure/" rel="nofollow" target="_blank">on The Data Exchange web site</a>.

Redefining AI Infrastructure: Deploying and Developing with a Next-Generation Developer Platform

Andrew Feldman is CEO and co-founder of Cerebras, a startup that has released the fastest AI accelerator, based on the largest processor. We discussed Cerebras-GPT, a family of language models that have set new benchmarks for accuracy and compute efficiency, with sizes ranging from 111 million to 13 billion parameters.Subscribe to the Gradient Flow Newsletter:  https://gradientflow.substack.com/Subscribe: Apple • Spotify • Stitcher • Google • AntennaPod • Podcast Addict • Amazon •  RSS.Detailed show notes can be found on The Data Exchange web site.

<a href="https://www.linkedin.com/in/andrewdfeldman/" rel="nofollow" target="_blank">Andrew Feldman</a> is CEO and co-founder of <a href="https://www.cerebras.net/" rel="nofollow" target="_blank">Cerebras, a startup</a> that has released the fastest AI accelerator, based on the largest processor. We discussed <a href="https://www.cerebras.net/blog/cerebras-gpt-a-family-of-open-compute-efficient-large-language-models/" rel="nofollow" target="_blank">Cerebras-GPT</a>, a family of language models that have set new benchmarks for accuracy and compute efficiency, with sizes ranging from 111 million to 13 billion parameters. Subscribe to the Gradient Flow Newsletter:  <a href="https://gradientflow.substack.com/" rel="nofollow" target="_blank">https://gradientflow.substack.com/</a> Subscribe: <a href="https://podcasts.apple.com/us/podcast/the-data-exchange-with-ben-lorica/id1487704458" rel="nofollow" target="_blank">Apple</a> • <a href="https://open.spotify.com/show/65oFWYoomwT8mLcdGkUGd0" rel="nofollow" target="_blank">Spotify</a> • <a href="https://www.stitcher.com/show/the-data-exchange" rel="nofollow" target="_blank">Stitcher</a> • <a href="https://podcasts.google.com/?feed=aHR0cHM6Ly9mZWVkcy5idXp6c3Byb3V0LmNvbS82ODI0MzMucnNz" rel="nofollow" target="_blank">Google</a> • <a href="https://antennapod.org/deeplink/subscribe/?url=feeds.buzzsprout.com/682433.rss" rel="nofollow" target="_blank">AntennaPod</a> • <a href="https://podcastaddict.com/podcast/2476584" rel="nofollow" target="_blank">Podcast Addict</a> • <a href="https://music.amazon.com/podcasts/18e76845-60a8-4263-9c41-a7bdfe9d14a9/the-data-exchange-with-ben-lorica" rel="nofollow" target="_blank">Amazon</a> •  <a href="https://feeds.buzzsprout.com/682433.rss" rel="nofollow" target="_blank">RSS</a>. Detailed show notes can be found <a href="https://thedataexchange.media/the-rise-of-custom-foundation-models/" rel="nofollow" target="_blank">on The Data Exchange web site</a>.

The Rise of Custom Foundation Models

Louis Brandy is  VP of Engineering at Rockset, the real-time search and analytics database startup formed by the creators of the popular open source project, RocksDB. Subscribe to the Gradient Flow Newsletter:  https://gradientflow.substack.com/Subscribe: Apple • Spotify • Stitcher • Google • AntennaPod • Podcast Addict • Amazon •  RSS.Detailed show notes can be found on The Data Exchange web site.

<a href="https://www.linkedin.com/in/lbrandy/" rel="nofollow" target="_blank">Louis Brandy</a> is  VP of Engineering at <a href="https://rockset.com/" rel="nofollow" target="_blank">Rockset</a>, the real-time search and analytics database startup formed by the creators of the popular open source project, <a href="https://rocksdb.org/" rel="nofollow" target="_blank">RocksDB</a>. Subscribe to the Gradient Flow Newsletter:  <a href="https://gradientflow.substack.com/" rel="nofollow" target="_blank">https://gradientflow.substack.com/</a> Subscribe: <a href="https://podcasts.apple.com/us/podcast/the-data-exchange-with-ben-lorica/id1487704458" rel="nofollow" target="_blank">Apple</a> • <a href="https://open.spotify.com/show/65oFWYoomwT8mLcdGkUGd0" rel="nofollow" target="_blank">Spotify</a> • <a href="https://www.stitcher.com/show/the-data-exchange" rel="nofollow" target="_blank">Stitcher</a> • <a href="https://podcasts.google.com/?feed=aHR0cHM6Ly9mZWVkcy5idXp6c3Byb3V0LmNvbS82ODI0MzMucnNz" rel="nofollow" target="_blank">Google</a> • <a href="https://antennapod.org/deeplink/subscribe/?url=feeds.buzzsprout.com/682433.rss" rel="nofollow" target="_blank">AntennaPod</a> • <a href="https://podcastaddict.com/podcast/2476584" rel="nofollow" target="_blank">Podcast Addict</a> • <a href="https://music.amazon.com/podcasts/18e76845-60a8-4263-9c41-a7bdfe9d14a9/the-data-exchange-with-ben-lorica" rel="nofollow" target="_blank">Amazon</a> •  <a href="https://feeds.buzzsprout.com/682433.rss" rel="nofollow" target="_blank">RSS</a>. Detailed show notes can be found <a href="https://thedataexchange.media/the-future-of-vector-databases-and-the-rise-of-instant-updates/" rel="nofollow" target="_blank">on The Data Exchange web site</a>.

The Future of Vector Databases and the Rise of Instant Updates

Amin Ahmad, the co-founder of Vectara, has played a crucial role in developing a powerful API platform specifically tailored for developers.Subscribe to the Gradient Flow Newsletter:  https://gradientflow.substack.com/Subscribe: Apple • Spotify • Stitcher • Google • AntennaPod • Podcast Addict • Amazon •  RSS.Detailed show notes can be found on The Data Exchange web site.

<a href="https://www.linkedin.com/in/aminahmad/" rel="nofollow" target="_blank">Amin Ahmad</a>, the co-founder of <a href="https://vectara.com/" rel="nofollow" target="_blank">Vectara</a>, has played a crucial role in developing a powerful API platform specifically tailored for developers. Subscribe to the Gradient Flow Newsletter:  <a href="https://gradientflow.substack.com/" rel="nofollow" target="_blank">https://gradientflow.substack.com/</a> Subscribe: <a href="https://podcasts.apple.com/us/podcast/the-data-exchange-with-ben-lorica/id1487704458" rel="nofollow" target="_blank">Apple</a> • <a href="https://open.spotify.com/show/65oFWYoomwT8mLcdGkUGd0" rel="nofollow" target="_blank">Spotify</a> • <a href="https://www.stitcher.com/show/the-data-exchange" rel="nofollow" target="_blank">Stitcher</a> • <a href="https://podcasts.google.com/?feed=aHR0cHM6Ly9mZWVkcy5idXp6c3Byb3V0LmNvbS82ODI0MzMucnNz" rel="nofollow" target="_blank">Google</a> • <a href="https://antennapod.org/deeplink/subscribe/?url=feeds.buzzsprout.com/682433.rss" rel="nofollow" target="_blank">AntennaPod</a> • <a href="https://podcastaddict.com/podcast/2476584" rel="nofollow" target="_blank">Podcast Addict</a> • <a href="https://music.amazon.com/podcasts/18e76845-60a8-4263-9c41-a7bdfe9d14a9/the-data-exchange-with-ben-lorica" rel="nofollow" target="_blank">Amazon</a> •  <a href="https://feeds.buzzsprout.com/682433.rss" rel="nofollow" target="_blank">RSS</a>. Detailed show notes can be found <a href="https://thedataexchange.media/llms-are-the-key-to-unlocking-the-next-generation-of-search/ " rel="nofollow" target="_blank">on The Data Exchange web site</a>.

LLMs Are the Key to Unlocking the Next Generation of Search

A series of informal conversations with thought leaders, researchers, practitioners, and writers on a wide range of topics in technology, science, and of course big data, data science, artificial intelligence, and related applications. Anchored by Ben Lorica (@BigData), the Data Exchange also features a roundup of the most important stories from the worlds of data, machine learning and AI. Detailed show notes for each episode can be found on https://thedataexchange.media/   The Data Exchange podcast is a production of Gradient Flow [https://gradientflow.com/].

Never a bad episode! Always engaging guests, Ben does an excellent job as a host! Keep up the great work!

Great Podcast!

A series of informal conversations with thought leaders, researchers, practitioners, and writers on a wide range of topics in technology, science, and of course big data, data science, artificial intelligence, and related applications. Anchored by Ben Lorica (@BigData), the Data Exchange also features a roundup of the most important stories from the worlds of data, machine learning and AI. Detailed show notes for each episode can be found on https://thedataexchange.media/ The Data Exchange podcast is a production of Gradient Flow [<a href="https://gradientflow.com/" rel="nofollow" target="_blank">https://gradientflow.com/</a>].

The Data Exchange with Ben Lorica

In this episode of the Data Exchange, I speak with my podcast co-organizer Mikio Braun, data scientist at GetYourGuide, and a former machine learning researcher and data architect. Mikio and I go out on a limb and speculate about new trends in AI and Data that we think people should pay attention to in 2020.Our conversation spanned many topics, and we listed trends in:Models: reinforcement learning, deep learning, language models, and related topics.Applications: including emerging use cases for reinforcement learning.Infrastructure and Tools: end-to-end machine learning platforms, the importance of distributed computing, etc.Managing risks: privacy, security, safety, fairness, etc.Emerging technologies to watch for in 2020.Detailed show notes can be found on The Data Exchange web site.

In this episode of the<a href="https://thedataexchange.media" rel="nofollow" target="_blank"> Data Exchange</a>, I speak with my podcast co-organizer <a href="https://www.linkedin.com/in/mikiobraun/" rel="nofollow" target="_blank">Mikio Braun</a>, data scientist at <a href="https://www.getyourguide.com/about" rel="nofollow" target="_blank">GetYourGuide</a>, and a former machine learning researcher and data architect. Mikio and I go out on a limb and speculate about new trends in AI and Data that we think people should pay attention to in 2020.Our conversation spanned many topics, and we listed trends in:<ul><li>Models: reinforcement learning, deep learning, language models, and related topics.</li><li>Applications: including emerging use cases for reinforcement learning.</li><li>Infrastructure and Tools: end-to-end machine learning platforms, the importance of distributed computing, etc.</li><li>Managing risks: privacy, security, safety, fairness, etc.</li><li>Emerging technologies to watch for in 2020.</li></ul>Detailed <a href="https://thedataexchange.media/key-ai-and-data-trends-for-2020" rel="nofollow" target="_blank">show notes can be found on The Data Exchange web site</a>.

Key AI and Data Trends for 2020

In this episode of the Data Exchange I speak with Rajat Monga, one of the founding members of the TensorFlow Engineering team. Up until recently Rajat was the engineering manager for TensorFlow at Google. Our conversation spanned many topics, including:TFX, a production scale machine learning platform based on TensorFlow.Distributed training.MLIR (Multi-Level Intermediate Representation), “a representation format and library of compiler utilities that sits between the model representation and low-level compilers/executors that generate hardware-specific code.”Deep learning in the enterprise.The state of machine learning infrastructure.[full show notes can be found on the Data Exchange web site.]

In this episode of the<a href="https://www.oreilly.com/ideas/topics/oreilly-data-show-podcast" rel="nofollow" target="_blank"> </a><a href="https://thedataexchange.media" rel="nofollow" target="_blank">Data Exchange</a> I speak with <a href="https://twitter.com/rajatmonga" rel="nofollow" target="_blank">Rajat Monga</a>, one of the founding members of the TensorFlow Engineering team. Up until recently Rajat was the engineering manager for TensorFlow at Google. Our conversation spanned many topics, including:<ul><li><a href="https://www.tensorflow.org/tfx" rel="nofollow" target="_blank">TFX</a>, a production scale machine learning platform based on TensorFlow.</li><li>Distributed training.</li><li><a href="https://medium.com/tensorflow/mlir-a-new-intermediate-representation-and-compiler-framework-beba999ed18d" rel="nofollow" target="_blank">MLIR</a> (Multi-Level Intermediate Representation), “a representation format and library of compiler utilities that sits between the model representation and low-level compilers/executors that generate hardware-specific code.”</li><li>Deep learning in the enterprise.</li><li>The state of machine learning infrastructure.</li></ul>[full show notes can be found <a href="https://thedataexchange.media/the-evolution-of-machine-learning-infrastructure" rel="nofollow" target="_blank">on the Data Exchange web site</a>.]

The evolution of TensorFlow and of machine learning infrastructure

In this episode of the Data Exchange I speak with Edo Liberty, founder of Hypercube, a startup building tools for deploying deep learning models in search and information retrieval involving large collections. When I spoke at AI Week in Tel Aviv last November several friends encouraged me to learn more about Hypercube - I’m glad I took their advice!Our conversation covered several topics including:Edo’s experience applying machine learning and building tools for ML at places like Yale, Yahoo's Research Lab in New York, and Amazon’s AI Lab.How deep learning is being used in search and information retrieval.Challenges one faces in building search and information retrieval applications when the size of collections are large.Deep learning based search and information retrieval and what Edo describes as “enterprise end-to-end deep search platforms”.Detailed show notes can be found on The Data Exchange web site.

In this episode of the <a href="https://thedataexchange.media" rel="nofollow" target="_blank">Data Exchange</a> I speak with <a href="https://www.linkedin.com/in/edo-liberty-4380164/" rel="nofollow" target="_blank">Edo Liberty</a>, founder of <a href="https://www.hypercube.ai/" rel="nofollow" target="_blank">Hypercube</a>, a startup building tools for deploying deep learning models in search and information retrieval involving large collections. When I spoke at <a href="https://www.facebook.com/pg/AIWeekTLV/photos/?ref=page_internal" rel="nofollow" target="_blank">AI Week in Tel Aviv</a> last November several friends encouraged me to learn more about Hypercube - I’m glad I took their advice! Our conversation covered several topics including:<ul><li>Edo’s experience applying machine learning and building tools for ML at places like Yale, Yahoo's Research Lab in New York, and Amazon’s AI Lab.</li><li>How deep learning is being used in search and information retrieval.</li><li>Challenges one faces in building search and information retrieval applications when the size of collections are large.</li><li>Deep learning based search and information retrieval and what Edo describes as “enterprise end-to-end deep search platforms”.</li></ul>Detailed <a href="https://thedataexchange.media/how-deep-learning-is-being-used-in-search-and-information-retrieval" rel="nofollow" target="_blank">show notes can be found on The Data Exchange web site</a>.

How deep learning is being used in search and information retrieval

Sijie Guo on how Apache Pulsar is able to handle both queuing and streaming, and both online and offline applications.In this episode of the Data Exchange I speak with Sijie Guo, founder of StreamNative, a new startup focused on making enterprise messaging technologies - specifically Apache Pulsar - easy to use on the cloud. Sijie was previously a cofounder of Streamlio (acquired by Splunk) and prior to that he led the messaging team at Twitter. He is also the main organizer behind the Pulsar Summit (April in San Francisco), a new conference whose Call for Speakers closes on January 31st.  Our conversation spanned many topics, including:The role of messaging in modern data applications and platforms.The two main types of messaging applications: queuing and streaming.Apache Pulsar as a unified messaging platform, able to handle both queuing and streaming, and both online and offline applications.A status update on Apache Pulsar.Detailed show notes can be found on The Data Exchange web site.

Sijie Guo on how Apache Pulsar is able to handle both queuing and streaming, and both online and offline applications. In this episode of the<a href="https://www.oreilly.com/ideas/topics/oreilly-data-show-podcast" rel="nofollow" target="_blank"> </a><a href="https://thedataexchange.media" rel="nofollow" target="_blank">Data Exchange</a> I speak with <a href="https://www.linkedin.com/in/samuelguo/" rel="nofollow" target="_blank">Sijie Guo</a>, founder of <a href="https://streamnative.io/" rel="nofollow" target="_blank">StreamNative</a>, a new startup focused on making enterprise messaging technologies - specifically <a href="https://pulsar.apache.org/" rel="nofollow" target="_blank">Apache Pulsar</a> - easy to use on the cloud. Sijie was previously a cofounder of Streamlio (<a href="https://www.idgconnect.com/news/1503244/splunk-makes-partner-enhancements-acquires-streamlio" rel="nofollow" target="_blank">acquired by Splunk</a>) and prior to that he led the messaging team at Twitter. He is also the main organizer behind the <a href="https://pulsar-summit.org/" rel="nofollow" target="_blank">Pulsar Summit</a> (April in San Francisco), a new conference whose Call for Speakers closes on January 31st.   Our conversation spanned many topics, including:<ul><li>The role of messaging in modern data applications and platforms.</li><li>The two main types of messaging applications: <a href="https://streamnative.io/blog/tech/2019-12-03-compare-pulsar-kafka/?src=thedataexchange" rel="nofollow" target="_blank">queuing and streaming</a>.</li><li>Apache Pulsar as a unified messaging platform, able to handle both queuing and streaming, and both online and offline applications.</li><li>A status update on Apache Pulsar.</li></ul>Detailed <a href="https://thedataexchange.media/taking-messaging-and-data-ingestion-systems-to-the-next-level" rel="nofollow" target="_blank">show notes can be found on The Data Exchange web site</a>.

Taking messaging and data ingestion systems to the next level

In this episode of the Data Exchange I speak with Edmon Begoli, Chief Data Architect at Oak Ridge National Laboratory (ORNL).  Edmon has developed and implemented large-scale data applications on systems like Open MPI, Hadoop/MapReduce, Apache Calcite, Apache Spark, and Akka. Most recently he has been building large-scale machine learning and natural language applications with Ray, a distributed execution framework that makes it easy to scale machine learning and Python applications.Our conversation included a range of topics, including:Edmon’s role at the ORNL and his experience building applications with Hadoop and Spark.What is distributed online learning?Why they started using Ray to build distributed online learning applications.Two important use cases: suicide prevention among US veterans and infectious disease surveillance.Detailed show notes can be found on The Data Exchange web site.Join Michael Jordan, Manuela Veloso, Azalia Mirhoseini, Zoubin Ghahramani, Wes McKinney, Ion Stoica, Gaël Varoquaux, and many other speakers at the first Ray Summit In San Francisco, May 27-28. Tickets start at $200.

In this episode of the <a href="https://thedataexchange.media" rel="nofollow" target="_blank">Data Exchange</a> I speak with <a href="https://www.olcf.ornl.gov/directory/staff-member/edmon-begoli/" rel="nofollow" target="_blank">Edmon Begoli</a>, Chief Data Architect at <a href="https://www.olcf.ornl.gov/" rel="nofollow" target="_blank">Oak Ridge National Laboratory</a> (ORNL).  Edmon has developed and implemented large-scale data applications on systems like Open MPI, Hadoop/MapReduce, Apache Calcite, Apache Spark, and <a href="https://github.com/akka/akka?src=thedataexchange" rel="nofollow" target="_blank">Akka</a>. Most recently he has been building large-scale machine learning and natural language applications with <a href="https://ray.io/?src=thedataexchange" rel="nofollow" target="_blank">Ray</a>, a distributed execution framework that makes it easy to scale machine learning and Python applications. Our conversation included a range of topics, including:<ul><li>Edmon’s role at the ORNL and his experience building applications with Hadoop and Spark.</li><li>What is distributed online learning?</li><li>Why they started using Ray to build distributed online learning applications.</li><li>Two important use cases: suicide prevention among US veterans and infectious disease surveillance.</li></ul>Detailed <a href="https://thedataexchange.media/hyperscaling-natural-language-processing" rel="nofollow" target="_blank">show notes can be found on The Data Exchange web site</a>. Join Michael Jordan, Manuela Veloso, Azalia Mirhoseini, Zoubin Ghahramani, Wes McKinney, Ion Stoica, Gaël Varoquaux, and many other speakers at the first <a href="https://events.linuxfoundation.org/ray-summit/?src=thedataexchange" rel="nofollow" target="_blank">Ray Summit In San Francisco, May 27-28</a>. Tickets start at $200.

Hyperscaling natural language processing

In this episode of the Data Exchange I speak with Rob Munro, CEO of Machine Learning Consulting and author of the forthcoming book, “Human-in-the-loop Machine Learning”. If you want a copy of Rob’s book, use the discount code podexchange20.Our conversation covered:Rob’s experience building data and machine learning products at Powerset, Idibon, and AWS.Natural language processing - Given Rob’s extensive experience as a researcher, practitioner, and entrepreneur in areas that touch on NLP, we discussed recent trends in language technologies.Human-in-the-loop machine learning.Our goal in this podcast is to build a community of people interested in Data, Machine Learning and AI. If you have suggestions for us on what to recommend (books, conferences, links), and guests to book, please visit TheDataExchange.media site and fill out the “contact” form.  The first five people who fill out the form get a free book from Manning (you can view Manning’s catalog here).Detailed show notes can be found on The Data Exchange web site.

In this episode of the <a href="https://thedataexchange.media" rel="nofollow" target="_blank">Data Exchange</a> I speak with <a href="http://www.robertmunro.com/" rel="nofollow" target="_blank">Rob Munro</a>, CEO of Machine Learning Consulting and author of the forthcoming book, <a href="https://www.manning.com/books/human-in-the-loop-machine-learning" rel="nofollow" target="_blank">“Human-in-the-loop Machine Learning”</a>. If you want a copy of Rob’s book, use the discount code podexchange20.Our conversation covered:<ul><li>Rob’s experience building data and machine learning products at <a href="https://en.wikipedia.org/wiki/Powerset_(company)" rel="nofollow" target="_blank">Powerset</a>, <a href="https://idibon.com/" rel="nofollow" target="_blank">Idibon</a>, and AWS.</li><li>Natural language processing - Given Rob’s extensive experience as a researcher, practitioner, and entrepreneur in areas that touch on NLP, we discussed recent trends in language technologies.</li><li>Human-in-the-loop machine learning.</li></ul>Our goal in this podcast is to build a community of people interested in Data, Machine Learning and AI. If you have suggestions for us on what to recommend (books, conferences, links), and guests to book, please visit <a href="https://thedataexchange.media/" rel="nofollow" target="_blank">TheDataExchange.media</a> site and fill out <a href="https://thedataexchange.media/contact/" rel="nofollow" target="_blank">the “contact” form</a>.  The first five people who fill out the form get a free book from Manning (you can view Manning’s catalog <a href="https://www.manning.com/catalog" rel="nofollow" target="_blank">here</a>). Detailed <a href="https://thedataexchange.media/human-in-the-loop-machine-learning" rel="nofollow" target="_blank">show notes can be found on The Data Exchange web site</a>.

Human-in-the-loop machine learning

In this episode of the Data Exchange I speak with Solmaz Shahalizadeh, VP and Head of Data Science and Data Platform Engineering at Shopify. Shopify is a powerhouse in ecommerce and their technology powers over a million businesses worldwide. Solmaz is a frequent speaker and presenter at conferences throughout the world and she has played a critical role in helping Shopify scale its data and machine learning infrastructure.Our conversation covered many important technical and business topics including:Building and scaling machine learning data products.Building and scaling data teams.Data informed product building.Detailed show notes can be found on The Data Exchange web site.

In this episode of the <a href="https://thedataexchange.media" rel="nofollow" target="_blank">Data Exchange</a> I speak with <a href="https://www.linkedin.com/in/solmazshahalizadeh/" rel="nofollow" target="_blank">Solmaz Shahalizadeh</a>, VP and Head of Data Science and Data Platform Engineering at <a href="https://www.shopify.com/" rel="nofollow" target="_blank">Shopify</a>. Shopify is a powerhouse in ecommerce and their technology powers over a million businesses worldwide. Solmaz is a frequent speaker and presenter at conferences throughout the world and she has played a critical role in helping Shopify scale its data and machine learning infrastructure. Our conversation covered many important technical and business topics including:<ul><li>Building and scaling machine learning data products.</li><li>Building and scaling data teams.</li><li>Data informed product building.</li></ul>Detailed <a href="https://thedataexchange.media/business-at-the-speed-of-ai-lessons-from-shopify" rel="nofollow" target="_blank">show notes can be found on The Data Exchange web site</a>.

Business at the speed of AI: Lessons from Shopify

In this episode of the Data Exchange I speak with Alejandro Saucedo, Engineering Director at Seldon, a startup building tools for productionizing machine learning. Alejandro is also Chief Scientist at The Institute for Ethical AI &amp; Machine Learning, a UK-based research center that conducts “research into processes and frameworks that support the responsible development, deployment and operation of machine learning systems”.Our conversation covered Alejandro’s work at both Seldon and the Institute for Ethical AI &amp; Machine Learning:We discussed topic areas that the Institute focuses on including explainability, MLOps, adversarial robustness, and privacy-preserving machine learningWe covered some of the recent output from the Institute including the machine learning maturity model, their open source explainable AI library, their AI-RFX Procurement Framework, and their list of Principles for Responsible AIWe also discussed his role at Seldon, and areas that Seldon has been focused on.Detailed show notes can be found on The Data Exchange web site.

In this episode of the <a href="https://thedataexchange.media" rel="nofollow" target="_blank">Data Exchange</a> I speak with Alejandro Saucedo, Engineering Director at <a href="https://www.seldon.io?src=thedataexchange" rel="nofollow" target="_blank">Seldon</a>, a startup building tools for productionizing machine learning. Alejandro is also Chief Scientist at <a href="https://ethical.institute?src=thedataexchange" rel="nofollow" target="_blank">The Institute for Ethical AI &amp; Machine Learning</a>, a UK-based research center that conducts “research into processes and frameworks that support the responsible development, deployment and operation of machine learning systems”. Our conversation covered Alejandro’s work at both Seldon and the Institute for Ethical AI &amp; Machine Learning:<ul><li>We discussed topic areas that the Institute focuses on including explainability, MLOps, adversarial robustness, and privacy-preserving machine learning</li><li>We covered some of the recent output from the Institute including the machine learning maturity model, their <a href="https://ethical.institute/xai.html?src=thedataexchange" rel="nofollow" target="_blank">open source explainable AI library</a>, their <a href="https://ethical.institute/rfx.html?src=thedataexchange" rel="nofollow" target="_blank">AI-RFX Procurement Framework</a>, and their <a href="https://ethical.institute/principles.html?src=thedataexchange" rel="nofollow" target="_blank">list of Principles for Responsible AI</a></li><li>We also discussed his role at Seldon, and areas that Seldon has been focused on.</li></ul>Detailed <a href="https://thedataexchange.media/the-responsible-development-deployment-and-operation-of-machine-learning-systems " rel="nofollow" target="_blank">show notes can be found on The Data Exchange web site</a>.

The responsible development, deployment and operation of machine learning systems

The Data Exchange Podcast: Bahman Bahmani on attracting and retaining talent, and the importance of delivery-oriented teams.In this episode of the Data Exchange I speak with Bahman Bahmani, VP of Data Science and Engineering at Rakuten, a large Japanese ecommerce and online retail company. When I first met Bahman several years ago, he was finishing up his Computer Science PhD at Stanford, and at the time he was giving technical talks on machine learning algorithms and their applications to computer security. Today he leads a large team at Rakuten, and in my opinion he has established an organizational structure, processes and an AI practice that other companies should study.Our conversation spanned many topics, including:The impact that AI, machine learning, and data have had on Rakuten’s businesses.Attracting, nurturing, and retaining talent in an environment when data scientists, data engineers, and analysts who all have many other options.The trio of strategic options: operational excellence, product leadership, customer intimacy.Organization and culture, including key roles within an AI practice.The power of delivery-oriented teams with end-to-end responsibility.Detailed show notes can be found on The Data Exchange web site.

The Data Exchange Podcast: Bahman Bahmani on attracting and retaining talent, and the importance of delivery-oriented teams. In this episode of the <a href="https://thedataexchange.media" rel="nofollow" target="_blank">Data Exchange</a> I speak with <a href="https://www.linkedin.com/in/bahmanibahman/" rel="nofollow" target="_blank">Bahman Bahmani</a>, VP of Data Science and Engineering at <a href="https://en.wikipedia.org/wiki/Rakuten" rel="nofollow" target="_blank">Rakuten</a>, a large Japanese ecommerce and online retail company. When I first met Bahman several years ago, he was finishing up his Computer Science PhD at Stanford, and at the time he was giving technical talks on machine learning algorithms and their applications to computer security. Today he leads a large team at Rakuten, and in my opinion he has established an organizational structure, processes and an AI practice that other companies should study.Our conversation spanned many topics, including:<ul><li>The impact that AI, machine learning, and data have had on Rakuten’s businesses.</li><li>Attracting, nurturing, and retaining talent in an environment when data scientists, data engineers, and analysts who all have many other options.</li><li>The trio of strategic options: operational excellence, product leadership, customer intimacy.</li><li>Organization and culture, including key roles within an AI practice.</li><li>The power of delivery-oriented teams with end-to-end responsibility.</li></ul>Detailed <a href="https://thedataexchange.media/business-at-the-speed-of-ai-lessons-from-rakuten" rel="nofollow" target="_blank">show notes can be found on The Data Exchange web site</a>.

Business at the speed of AI: Lessons from Rakuten

In this episode of the Data Exchange I speak with Dafna Shahaf, Associate Professor at the School of Computer Science and Engineering, the Hebrew University of Jerusalem. She also runs the hyadata lab, a research group that consistently produces unique and interesting projects at the intersection of computer science, data, and the social sciences.Our conversation included a range of topics, including:Computational analogy: Dafna and her students mine online sources like patent filings, research papers, and data from crowdsourcing platforms focused on innovation, and in the process they produce tools that should be of interest to innovation officers and members of innovation labs.Soft Concepts: Dafna has continued her work on computational humor, and along with her students, they have new tools for automatically finding trivia facts in Wikipedia.An upcoming workshop on Innovative Ideas in Data Science (April 20th in Taipei; the deadline to submit proposals is: 21 February 2020).Detailed show notes can be found on The Data Exchange web site.

In this episode of the <a href="https://thedataexchange.media" rel="nofollow" target="_blank">Data Exchange</a> I speak with <a href="https://www.cse.huji.ac.il/~dshahaf/" rel="nofollow" target="_blank">Dafna Shahaf</a>, Associate Professor at the School of Computer Science and Engineering, the Hebrew University of Jerusalem. She also runs the <a href="http://www.hyadatalab.com/" rel="nofollow" target="_blank">hyadata lab</a>, a research group that consistently produces unique and interesting projects at the intersection of computer science, data, and the social sciences.Our conversation included a range of topics, including:<ul><li>Computational analogy: Dafna and her students mine online sources like patent filings, research papers, and data from crowdsourcing platforms focused on innovation, and in the process they produce tools that should be of interest to innovation officers and members of innovation labs.</li><li>Soft Concepts: Dafna has continued her work on <a href="http://www.hyadatalab.com/research.html#Soft_Concepts" rel="nofollow" target="_blank">computational humor</a>, and along with her students, they have new tools for automatically finding trivia facts in Wikipedia.</li><li>An upcoming workshop on <a href="http://hyadatalab.com/IID2020/" rel="nofollow" target="_blank">Innovative Ideas in Data Science</a> (April 20th in Taipei; the deadline to submit proposals is: 21 February 2020).</li></ul>Detailed <a href="https://thedataexchange.media/computational-humanness-analogy-and-innovation-and-soft-concepts" rel="nofollow" target="_blank">show notes can be found on The Data Exchange web site</a>.

Computational humanness, analogy and innovation, and soft concepts

In this episode of the Data Exchange I speak with Krishna Gade, founder and CEO at Fiddler Labs, a startup focused on helping companies build trustworthy and understandable AI solutions. Prior to founding Fiddler, Krishna led engineering teams at Pinterest and Facebook.Our conversation included a range of topics, including:Krishna’s background as an engineering manager at Facebook and Pinterest.Why Krishna decided to start a company focused on explainability.Guidelines for companies who want to begin working on incorporating model explainability into their data products.The relationship between model explainability (transparency) and security (ML that can resist adversarial attacks).Detailed show notes can be found on The Data Exchange web site.Join Michael Jordan, Manuela Veloso, Azalia Mirhoseini, Zoubin Ghahramani, Wes McKinney, Ion Stoica, Gaël Varoquaux, and many other speakers at the first Ray Summit In San Francisco, May 27-28. Tickets start at $200.

In this episode of the <a href="https://thedataexchange.media" rel="nofollow" target="_blank">Data Exchange</a> I speak with <a href="https://www.linkedin.com/in/krishnagade/" rel="nofollow" target="_blank">Krishna Gade</a>, founder and CEO at <a href="https://www.fiddler.ai/" rel="nofollow" target="_blank">Fiddler Labs</a>, a startup focused on helping companies build trustworthy and understandable AI solutions. Prior to founding Fiddler, Krishna led engineering teams at Pinterest and Facebook. Our conversation included a range of topics, including:<ul><li>Krishna’s background as an engineering manager at Facebook and Pinterest.</li><li>Why Krishna decided to start a company focused on explainability.</li><li>Guidelines for companies who want to begin working on incorporating model explainability into their data products.</li><li>The relationship between model explainability (transparency) and security (ML that can resist adversarial attacks).</li></ul>Detailed <a href="https://thedataexchange.media/what-businesses-need-to-know-about-model-explainability" rel="nofollow" target="_blank">show notes can be found on The Data Exchange web site</a>. Join Michael Jordan, Manuela Veloso, Azalia Mirhoseini, Zoubin Ghahramani, Wes McKinney, Ion Stoica, Gaël Varoquaux, and many other speakers at the first <a href="https://events.linuxfoundation.org/ray-summit/?src=thedataexchange" rel="nofollow" target="_blank">Ray Summit In San Francisco, May 27-28</a>. Tickets start at $200.

What businesses need to know about model explainability

In this episode of the Data Exchange I speak with Morten Dahl, research scientist at Dropout Labs, a startup building a platform and tools for privacy-preserving machine learning. He is also behind TF Encrypted, an open source framework for encrypted machine learning in TensorFlow.  The rise of privacy regulations like CCPA and GDPR combined with the growing importance of ML has led to a strong interest in tools and techniques for privacy-preserving machine learning among researchers and practitioners. Morten brings the unique perspective of being a longtime security researcher who has also worked as a data scientist in industry.Our conversation spanned many topics, including:Morten’s unique background as an experienced security researcher, developer, and data scientist.The current state of TF Encrypted.Federated learning (FL) and secure aggregation for FL.Privacy-preserving ML solutions will employ a variety of techniques, and thus we also discussed related topics such as differential privacy, homomorphic encryption, and RISELab’s stack for coopetitive learning (MC2).Detailed show notes can be found on The Data Exchange web site.

In this episode of the <a href="https://thedataexchange.media" rel="nofollow" target="_blank">Data Exchange</a> I speak with <a href="https://www.linkedin.com/in/mortendahlcs/" rel="nofollow" target="_blank">Morten Dahl</a>, research scientist at <a href="https://dropoutlabs.com/" rel="nofollow" target="_blank">Dropout Labs</a>, a startup building a platform and tools for privacy-preserving machine learning. He is also behind <a href="https://github.com/tf-encrypted/tf-encrypted" rel="nofollow" target="_blank">TF Encrypted</a>, an open source framework for encrypted machine learning in TensorFlow.  The rise of privacy regulations like CCPA and GDPR combined with the growing importance of ML has led to a strong interest in tools and techniques for privacy-preserving machine learning among researchers and practitioners. Morten brings the unique perspective of being a longtime security researcher who has also worked as a data scientist in industry.Our conversation spanned many topics, including:<ul><li>Morten’s unique background as an experienced security researcher, developer, and data scientist.</li><li>The current state of TF Encrypted.</li><li>Federated learning (FL) and secure aggregation for FL.</li><li>Privacy-preserving ML solutions will employ a variety of techniques, and thus we also discussed related topics such as differential privacy, homomorphic encryption, and RISELab’s <a href="https://github.com/mc2-project" rel="nofollow" target="_blank">stack for coopetitive learning (MC2)</a>.</li></ul>Detailed <a href="https://thedataexchange.media/the-state-of-privacy-preserving-machine-learning" rel="nofollow" target="_blank">show notes can be found on The Data Exchange web site</a>.

The state of privacy-preserving machine learning

In this episode of the Data Exchange I speak with Nir Shavit, Professor of EECS at MIT, and cofounder and CEO of Neural Magic, a startup that is creating software to enable deep neural networks to run on commodity CPUs (at GPU speeds or faster). Their initial products are focused on model inference, but they are also working on similar software for model training.Our conversation spanned many topics, including:Neurobiology, in particular the combination of Nir’s research areas of multicore software and connectomics – a branch of neurobiology.Why he believes the combination of the right software and CPUs will prove capable of handling many deep learning tasks.Speed is not the only factor: the “unlimited memory” of CPUs are able to unlock larger problems and architectures.Neural Magic’s initial offering is in inference, model training using CPUs is also on the horizon.Detailed show notes can be found on The Data Exchange web site.

In this episode of the <a href="https://thedataexchange.media" rel="nofollow" target="_blank">Data Exchange</a> I speak with <a href="https://people.csail.mit.edu/shanir/" rel="nofollow" target="_blank">Nir Shavit</a>, Professor of EECS at MIT, and cofounder and CEO of <a href="https://neuralmagic.com/about/" rel="nofollow" target="_blank">Neural Magic</a>, a startup that is creating software to enable deep neural networks to run on commodity CPUs (at GPU speeds or faster). Their initial products are <a href="https://neuralmagic.com/blog/announcing-the-neural-magic-inference-engine/" rel="nofollow" target="_blank">focused on model inference</a>, but they are also working on similar software for model training.Our conversation spanned many topics, including:<ul><li>Neurobiology, in particular the combination of Nir’s research areas of multicore software and connectomics – a branch of neurobiology.</li><li>Why he believes the combination of the right software and CPUs will prove capable of handling many deep learning tasks.</li><li>Speed is not the only factor: the “unlimited memory” of CPUs are able to unlock larger problems and architectures.</li><li>Neural Magic’s initial offering is in inference, model training using CPUs is also on the horizon.</li></ul>Detailed <a href="https://thedataexchange.media/software-and-commodity-hardware-can-handle-deep-learning" rel="nofollow" target="_blank">show notes can be found on The Data Exchange web site</a>.

The combination of the right software and commodity hardware will prove capable of handling most machine learning tasks

In this episode of the Data Exchange I speak with David Talby, co-creator of Spark NLP, an open source, highly scalable, production grade natural language processing (NLP) library. Spark NLP has become one of the more popular NLP libraries and is available on PyPI, Conda, Maven, and Spark Packages. With recent advances in research in large-scale natural language models, there is strong interest in domain specific natural language applications. Besides their work on Spark NLP, David and his collaborators are building natural language models tuned specifically for healthcare applications.Our conversation spanned many topics, including:Spark NLP: its current status and some common and surprising use cases.Recent developments in NLP research and their implications for companies.Spark NLP for HealthcareDetailed show notes can be found on The Data Exchange web site.

In this episode of the <a href="https://thedataexchange.media" rel="nofollow" target="_blank">Data Exchange</a> I speak with <a href="https://www.linkedin.com/in/davidtalby/" rel="nofollow" target="_blank">David Talby</a>, co-creator of <a href="https://nlp.johnsnowlabs.com/" rel="nofollow" target="_blank">Spark NLP</a>, an open source, highly scalable, production grade natural language processing (NLP) library. Spark NLP has become one of the more popular NLP libraries and is available on PyPI, Conda, Maven, and Spark Packages. With recent advances in research in large-scale natural language models, there is strong interest in domain specific natural language applications. Besides their work on Spark NLP, David and his collaborators are building natural language models tuned specifically for healthcare applications.Our conversation spanned many topics, including:<ul><li>Spark NLP: its current status and some common and surprising use cases.</li><li>Recent developments in NLP research and their implications for companies.</li><li>Spark NLP for Healthcare</li></ul>Detailed <a href="https://thedataexchange.media/building-domain-specific-natural-language-applications" rel="nofollow" target="_blank">show notes can be found on The Data Exchange web site</a>.

Building domain specific natural language applications

In this episode of the Data Exchange I speak with Chris Nicholson, founder and CEO of Pathmind, a startup applying deep reinforcement learning (DRL) to simulation problems.  In a recent post I highlighted two areas where companies can begin to add DRL to their suite of tools: personalization and recommendation engines, and simulation software. My interest in the interplay between DRL and simulation software began when I came across the work of Pathmind in this area.Our conversation focused on deep reinforcement learning and its applications:We began with the basics: what is reinforcement learning and why should businesses pay attention to it?We discussed enterprise applications of DRL, with particular emphasis in areas where Chris and Pathmind have been focused of late: Business Process Simulation and Optimization.Pathmind have been early adopters of Ray and of RLlib, a popular open-source library for reinforcement learning built on top of Ray. I asked Chris why they chose to build on top of RLlib.Detailed show notes can be found on The Data Exchange web site.

In this episode of the <a href="https://thedataexchange.media" rel="nofollow" target="_blank">Data Exchange</a> I speak with <a href="https://www.linkedin.com/in/chrisvnicholson/" rel="nofollow" target="_blank">Chris Nicholson</a>, founder and CEO of <a href="https://pathmind.com/" rel="nofollow" target="_blank">Pathmind</a>, a startup applying deep reinforcement learning (DRL) to simulation problems.  In a recent post I highlighted two areas where companies can begin to add DRL to their suite of tools: personalization and recommendation engines, and simulation software. My interest in the interplay between DRL and simulation software began when I came across the work of Pathmind in this area. Our conversation focused on deep reinforcement learning and its applications:<ul><li>We began with the basics: what is reinforcement learning and why should businesses pay attention to it?</li><li>We discussed enterprise applications of DRL, with particular emphasis in areas where Chris and Pathmind have been focused of late: Business Process Simulation and Optimization.</li><li>Pathmind have been early adopters of <a href="https://ray.io/" rel="nofollow" target="_blank">Ray</a> and of <a href="https://ray.readthedocs.io/en/latest/rllib.html" rel="nofollow" target="_blank">RLlib</a>, a popular open-source library for reinforcement learning built on top of Ray. I asked Chris why they chose to build on top of RLlib.</li></ul>Detailed <a href="https://thedataexchange.media/next-generation-simulation-software-will-incorporate-deep-reinforcement-learning" rel="nofollow" target="_blank">show notes can be found on The Data Exchange web site</a>.

Next-generation simulation software will incorporate deep reinforcement learning

In this episode of the Data Exchange I speak with Bruno Gonçalves, a data scientist working at the intersection of Data Science and Finance. I have known Bruno for several years and we met when I recruited him to teach several extremely popular conference tutorials and talks on machine learning and deep learning. Prior to shifting over to data science, he spent several years as a researcher focused on mathematical models in Epidemiology – a field with a rich history dating as far back as the 1920s.  This episode is devoted to tools and techniques for modeling epidemics.Our conversation covered:Bruno’s background and his experience in modeling epidemics.The field of epidemic models: what techniques are used, the size of the community of researchers, and how do models get evaluated.His two recent posts: “Epidemic Modeling 101 – Or why your CoVID-19 exponential fits are wrong” and  “Epidemic Modeling 102 – All CoVID-19 models are wrong, but some are useful”The role that epidemic models play in decision making.Detailed show notes can be found on The Data Exchange web site.Subscribe to The Data Exchange Newsletter.

In this episode of the <a href="https://thedataexchange.media" rel="nofollow" target="_blank">Data Exchange</a> I speak with <a href="http://www.bgoncalves.com/" rel="nofollow" target="_blank">Bruno Gonçalves</a>, a data scientist working at the intersection of Data Science and Finance. I have known Bruno for several years and we met when I recruited him to teach several extremely popular conference tutorials and talks on machine learning and deep learning. Prior to shifting over to data science, he spent several years as a researcher focused on mathematical models in Epidemiology – a field with a rich history dating as far back as the 1920s.  This episode is devoted to tools and techniques for modeling epidemics. Our conversation covered:<ul><li>Bruno’s background and his experience in modeling epidemics.</li><li>The field of epidemic models: what techniques are used, the size of the community of researchers, and how do models get evaluated.</li><li>His two recent posts: <a href="https://medium.com/data-for-science/epidemic-modeling-101-or-why-your-covid19-exponential-fits-are-wrong-97aa50c55f8" rel="nofollow" target="_blank">“Epidemic Modeling 101 – Or why your CoVID-19 exponential fits are wrong”</a> and  <a href="https://medium.com/data-for-science/epidemic-modeling-102-all-covid-19-models-are-wrong-but-some-are-useful-c81202cc6ee9" rel="nofollow" target="_blank">“Epidemic Modeling 102 – All CoVID-19 models are wrong, but some are useful”</a></li><li>The role that epidemic models play in decision making.</li></ul>Detailed <a href="https://thedataexchange.media/computational-models-and-simulations-of-epidemic-infectious-diseases/" rel="nofollow" target="_blank">show notes can be found on The Data Exchange web site</a>. Subscribe to <a href="https://dataexchange.substack.com/" rel="nofollow" target="_blank">The Data Exchange Newsletter</a>.

Computational Models and Simulations of Epidemic Infectious Diseases

In this episode of the Data Exchange I speak with Dean Wampler, Head of Developer Relations at Anyscale, the startup founded by the creators of Ray. Ray is a distributed execution framework that makes it easy to scale machine learning and Python applications. It has a very simple API and as someone who uses both Python and machine learning, Ray has been a wonderful addition to my toolbox.  Dean has long been one of my favorite architects, speakers and teachers, and we have known each other since the early days of Apache Spark. He has authored numerous books and is known for his interest in Scala and programming languages, as well as in software architecture.Our conversation spanned many topics, including:What is Ray and why should someone consider using it?The first Ray Summit (May 27-28 in San Francisco)Dean’s first impressions of Ray, and his journey from Scala to Python.An update on Ray’s core libraries, Ray on Windows, and distributed training with Ray.Detailed show notes can be found on The Data Exchange web site.For more on Ray and scalable machine learning &amp; Python, come hear from Dean Wampler, Michael Jordan, Ion Stoica, Manuela Veloso, Wes McKinney and many other leading developers and researchers at the first Ray Summit in San Francisco (May 27-28).

In this episode of the <a href="https://thedataexchange.media" rel="nofollow" target="_blank">Data Exchange</a> I speak with <a href="https://www.linkedin.com/in/deanwampler/" rel="nofollow" target="_blank">Dean Wampler</a>, Head of Developer Relations at <a href="http://anyscale.io/" rel="nofollow" target="_blank">Anyscale</a>, the startup founded by the creators of <a href="https://ray.io" rel="nofollow" target="_blank">Ray</a>. Ray is a distributed execution framework that makes it easy to scale machine learning and Python applications. It has a very simple API and as someone who uses both Python and machine learning, Ray has been a wonderful addition to my toolbox.  Dean has long been one of my favorite architects, speakers and teachers, and we have known each other since the early days of Apache Spark. He has <a href="https://www.amazon.com/s?i=stripbooks&amp;rh=p_27%3ADean+Wampler&amp;s=relevancerank&amp;text=Dean+Wampler&amp;ref=dp_byline_sr_book_1" rel="nofollow" target="_blank">authored numerous books</a> and is known for his interest in Scala and programming languages, as well as in software architecture. Our conversation spanned many topics, including:<ul><li>What is Ray and why should someone consider using it?</li><li>The first <a href="http://raysummit.org" rel="nofollow" target="_blank">Ray Summit</a> (May 27-28 in San Francisco)</li><li>Dean’s first impressions of Ray, and his journey from Scala to Python.</li><li>An update on Ray’s core libraries, Ray on Windows, and distributed training with Ray.</li></ul>Detailed <a href="https://thedataexchange.media/scalable-machine-learning-scalable-python-for-everyone" rel="nofollow" target="_blank">show notes can be found on The Data Exchange web site</a>. For more on Ray and scalable machine learning &amp; Python, come hear from Dean Wampler, Michael Jordan, Ion Stoica, Manuela Veloso, Wes McKinney and many other leading developers and researchers at the first <a href="http://raysummit.org" rel="nofollow" target="_blank">Ray Summit</a> in San Francisco (May 27-28).

Scalable Machine Learning, Scalable Python, For Everyone

In this episode of the Data Exchange our special correspondent and editor Jenn Webb speaks with Arum Verma, Head of Quantitative Research Solutions at Bloomberg. My first job post-academia was as lead quant in a small hedge fund. Since then, I’ve followed the industry from afar and I’ve long been interested in the role of data and models in financial services. Arun and I discussed quantitative finance when we ran into each other at the O’Reilly AI conference in London last year. He was slated to give a talk on extracting trading signals from alternative data sets, an important subject among quants.Jenn and Arun discussed a range of topics including:The quantitative finance landscape.The challenges in identifying and using alternative data sources.Applications of machine learning in finance, specifically deep learning and reinforcement learning.New natural language models and their applications in finance.Model Explainability and Model Risk Management.Detailed show notes can be found on The Data Exchange web site.Subscribe to The Gradient Flow Newsletter.

In this episode of the <a href="https://thedataexchange.media" rel="nofollow" target="_blank">Data Exchange</a> our special correspondent and editor <a href="https://www.linkedin.com/in/jennwebb/" rel="nofollow" target="_blank">Jenn Webb</a> speaks with <a href="https://www.linkedin.com/in/arun-verma-0858b8/" rel="nofollow" target="_blank">Arum Verma</a>, Head of Quantitative Research Solutions at Bloomberg. My first job post-academia was as lead quant in a small hedge fund. Since then, I’ve followed the industry from afar and I’ve long been interested in the role of data and models in financial services. Arun and I discussed quantitative finance when we ran into each other at the O’Reilly AI conference in London last year. He was slated to give <a href="https://conferences.oreilly.com/artificial-intelligence/ai-eu-2019/public/schedule/speaker/295510" rel="nofollow" target="_blank">a talk</a> on extracting trading signals from alternative data sets, an important subject among quants. Jenn and Arun discussed a range of topics including:<ul><li>The quantitative finance landscape.</li><li>The challenges in identifying and using alternative data sources.</li><li>Applications of machine learning in finance, specifically deep learning and reinforcement learning.</li><li>New natural language models and their applications in finance.</li><li>Model Explainability and Model Risk Management.</li></ul>Detailed <a href="https://thedataexchange.media/how-machine-learning-is-being-used-in-quantitative-finance/" rel="nofollow" target="_blank">show notes can be found on The Data Exchange web site</a>. Subscribe to <a href="https://gradientflow.substack.com/subscribe" rel="nofollow" target="_blank">The Gradient Flow Newsletter</a>.

How machine learning is being used in quantitative finance

In this episode of the Data Exchange I speak with Harish Doddi, cofounder of Datatron, a startup focused on helping companies operationalize machine learning. Over the past two years, Harish has worked closely with enterprises to understand their needs in the areas of model operations and model governance. Last year Harish and I, along with David Talby, wrote two articles on these topics. In the first article, we described these emerging areas (“What are model governance and model operations?”),  and in the second we listed lessons that ML engineers can draw from two highly regulated industries (“Managing machine learning in the enterprise: Lessons from banking and health care”).As machine learning becomes widely deployed, organizations will need to develop processes and tools to ensure that models behave as intended. This means having the right set of controls and validation steps in place.Our conversation focused on model governance and related topics:We discussed the three related areas of MLOps, Model Governance, Model Observability.I asked Harish to describe how model governance is perceived and practiced in different industries.We discussed real-world examples of model governance, and organizational and staffing considerations that come into play.CI/CD for machine learning.Key enterprise features for model governance solutions.Detailed show notes, including a full transcript, can be found on The Data Exchange web site.Subscribe to The Gradient Flow Newsletter.

In this episode of the <a href="https://thedataexchange.media" rel="nofollow" target="_blank">Data Exchange</a> I speak with <a href="https://www.linkedin.com/in/harishd/" rel="nofollow" target="_blank">Harish Doddi</a>, cofounder of <a href="https://www.datatron.com/" rel="nofollow" target="_blank">Datatron</a>, a startup focused on helping companies operationalize machine learning. Over the past two years, Harish has worked closely with enterprises to understand their needs in the areas of model operations and model governance. Last year Harish and I, along with David Talby, wrote two articles on these topics. In the first article, we described these emerging areas (<a href="https://www.oreilly.com/radar/what-are-model-governance-and-model-operations/" rel="nofollow" target="_blank">“What are model governance and model operations?”</a>),  and in the second we listed lessons that ML engineers can draw from two highly regulated industries (<a href="https://www.oreilly.com/radar/managing-machine-learning-in-the-enterprise-lessons-from-banking-and-health-care/" rel="nofollow" target="_blank">“Managing machine learning in the enterprise: Lessons from banking and health care”</a>).As machine learning becomes widely deployed, organizations will need to develop processes and tools to ensure that models behave as intended. This means having the right set of controls and validation steps in place.Our conversation focused on model governance and related topics:<ul><li>We discussed the three related areas of MLOps, Model Governance, Model Observability.</li><li>I asked Harish to describe how model governance is perceived and practiced in different industries.</li><li>We discussed real-world examples of model governance, and organizational and staffing considerations that come into play.</li><li>CI/CD for machine learning.</li><li>Key enterprise features for model governance solutions.</li></ul>Detailed <a href="https://thedataexchange.media/understanding-machine-learning-model-governance/" rel="nofollow" target="_blank">show notes, including a full transcript, can be found on The Data Exchange web site</a>. Subscribe to <a href="https://gradientflow.substack.com/subscribe" rel="nofollow" target="_blank">The Gradient Flow Newsletter</a>.

Understanding machine learning model governance

In this episode of the Data Exchange I speak with Wes McKinney, Director of Ursa Labs and an Apache Arrow PMC Member. Wes is the creator of pandas, one of the most widely used Python libraries for data science. He is also the author of the best-selling book, “Python for Data Analysis” – a book that has become essential reading for both aspiring and experienced data scientists.Our conversation focused on data science tools and other topics including:Two open source projects Wes has long been associated with: pandas and Apache Arrow.The need for a shared infrastructure for data science.Ursa Labs: its mission and structure.Detailed show notes can be found on The Data Exchange web site.Subscribe to The Gradient Flow Newsletter.

In this episode of the <a href="https://thedataexchange.media" rel="nofollow" target="_blank">Data Exchange</a> I speak with <a href="https://www.linkedin.com/in/wesmckinn/" rel="nofollow" target="_blank">Wes McKinney</a>, Director of <a href="https://ursalabs.org/" rel="nofollow" target="_blank">Ursa Labs</a> and an <a href="https://arrow.apache.org/" rel="nofollow" target="_blank">Apache Arrow</a> PMC Member. Wes is the <a href="https://en.wikipedia.org/wiki/Pandas_(software)" rel="nofollow" target="_blank">creator of pandas</a>, one of the most widely used Python libraries for data science. He is also the author of the best-selling book, <a href="https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython-ebook/dp/B075X4LT6K/ref=sr_1_1?dchild=1&amp;keywords=wes+mckinney&amp;qid=1588186655&amp;sr=8-1" rel="nofollow" target="_blank">“Python for Data Analysis”</a> – a book that has become essential reading for both aspiring and experienced data scientists. Our conversation focused on data science tools and other topics including:<ul><li>Two open source projects Wes has long been associated with: pandas and Apache Arrow.</li><li>The need for a shared infrastructure for data science.</li><li>Ursa Labs: its mission and structure.</li></ul> Detailed <a href="https://thedataexchange.media/improving-performance-and-scalability-of-data-science-libraries/" rel="nofollow" target="_blank">show notes can be found on The Data Exchange web site</a>. Subscribe to <a href="https://gradientflow.substack.com/subscribe" rel="nofollow" target="_blank">The Gradient Flow Newsletter</a>.

Improving performance and scalability of data science libraries

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