Practical AI: Machine Learning, Data Science
Technology:Software How-To
In this episode we welcome back our good friend Demetrios from the MLOps Community to discuss fine-tuning vs. retrieval augmented generation. Along the way, we also chat about OpenAI Enterprise, results from the MLOps Community LLM survey, and the orchestration and evaluation of generative AI workloads.
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Something missing or broken? PRs welcome!
Timestamps:
(00:07) - Welcome to Practical AI
(00:43) - Practical AI & Friends
(02:01) - Look into MLOps community
(04:19) - Changes in the AI community
(07:34) - Finding the norm
(08:30) - Matching models & uses
(11:21) - Stages of debugging
(13:10) - Layer orchestration
(16:26) - Practical hot takes
(21:46) - Fine-tuning is more work
(24:13) - Retrieval augmented generation
(31:23) - MLOps survey
(38:09) - The next survey
(41:50) - Enterprise hypetrain
(42:39) - OpenAI & your data
(43:19) - AI vendor lock-in?
(47:44) - Now what do we do?
(48:58) - Hype in the AI life
(56:35) - Goodbye
(57:23) - Outro
Rise of the AI PC & local LLMs
AI in the U.S. Congress
First impressions of GPT-4o
Full-stack approach for effective AI agents
Autonomous fighter jets?!
Private, open source chat UIs
Mamba & Jamba
Udio & the age of multi-modal AI
RAG continues to rise
Should kids still learn to code?
AI vs software devs
Prompting the future
Generating the future of art & entertainment
YOLOv9: Computer vision is alive and well
Representation Engineering (Activation Hacking)
Leading the charge on AI in National Security
Gemini vs OpenAI
Data synthesis for SOTA LLMs
Large Action Models (LAMs) & Rabbits 🐇
Collaboration & evaluation for LLM apps
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