Today we continue our coverage of the AWS ML Summit joined by Chris Fregly, a principal developer advocate at AWS, and Antje Barth, a senior developer advocate at AWS.
In our conversation with Chris and Antje, we explore their roles as community builders prior to, and since, joining AWS, as well as their recently released book Data Science on AWS. In the book, Chris and Antje demonstrate how to reduce cost and improve performance while successfully building and deploying data science projects.
We also discuss the release of their new Practical Data Science Specialization on Coursera, managing the complexity that comes with building real-world projects, and some of their favorite sessions from the recent ML Summit.
Powering AI with the World's Largest Computer Chip with Joel Hestness - #684
AI for Power & Energy with Laurent Boinot - #683
Controlling Fusion Reactor Instability with Deep Reinforcement Learning with Aza Jalalvand - #682
GraphRAG: Knowledge Graphs for AI Applications with Kirk Marple - #681
Teaching Large Language Models to Reason with Reinforcement Learning with Alex Havrilla - #680
Localizing and Editing Knowledge in LLMs with Peter Hase - #679
Coercing LLMs to Do and Reveal (Almost) Anything with Jonas Geiping - #678
V-JEPA, AI Reasoning from a Non-Generative Architecture with Mido Assran - #677
Video as a Universal Interface for AI Reasoning with Sherry Yang - #676
Assessing the Risks of Open AI Models with Sayash Kapoor - #675
OLMo: Everything You Need to Train an Open Source LLM with Akshita Bhagia - #674
Training Data Locality and Chain-of-Thought Reasoning in LLMs with Ben Prystawski - #673
Reasoning Over Complex Documents with DocLLM with Armineh Nourbakhsh - #672
Are Emergent Behaviors in LLMs an Illusion? with Sanmi Koyejo - #671
AI Trends 2024: Reinforcement Learning in the Age of LLMs with Kamyar Azizzadenesheli - #670
Building and Deploying Real-World RAG Applications with Ram Sriharsha - #669
Nightshade: Data Poisoning to Fight Generative AI with Ben Zhao - #668
Learning Transformer Programs with Dan Friedman - #667
AI Trends 2024: Machine Learning & Deep Learning with Thomas Dietterich - #666
AI Trends 2024: Computer Vision with Naila Murray - #665
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