Practical AI: Machine Learning, Data Science
Technology
We’ve been hearing about “serverless” CPUs for some time, but it’s taken a while to get to serverless GPUs. In this episode, Erik from Banana explains why its taken so long, and he helps us understand how these new workflows are unlocking state-of-the-art AI for application developers. Forget about servers, but don’t forget to listen to this one!
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Sponsors:
Featuring:
Show Notes:
Banana - Scale your machine learning inference and training on serverless GPUs.
Something missing or broken? PRs welcome!
Timestamps:
(00:07) - Welcome to Practical AI
(00:43) - Erik Dunteman
(02:34) - What does serverless mean to you?
(07:17) - What's the secret sauce?
(09:30) - How does serverless affect our workflows?
(13:21) - Sponsor: Changelog++
(14:21) - What languages do you prefer?
(17:20) - The Banana workflow
(20:33) - The necessary minimum skills to use Banana
(24:51) - A typical win
(26:20) - Incompatible workflows
(30:11) - Future Banana-AWS compatability?
(32:53) - Tips to choose your GPU
(35:23) - What's the future of serverless?
(37:46) - Outro
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
Advent of GenAI Hackathon recap
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