Designing for Forward Compatibility in Gen AI // Rohit Agarwal // #189

MLOps podcast #189 with Rohit Agarwal, CEO of Portkey.ai, Designing for Forward Compatibility in Gen AI.

// Abstract

For two whole years of working with a large LLM deployment, I always felt uncomfortable. How is my system performing? Are my users liking the outputs? Who needs help? Probabilistic systems can make this really hard to understand. In this talk, we'll discuss practical & implementable items to secure your LLM system and gain confidence while deploying to production.// BioRohit is the Co-founder and CEO of portkey.ai, which is an FMOps stack for monitoring, model management, compliance, and more. Previously, he headed Product & AI at Pepper Content, which has served ~900M generations on LLMs in production. Having seen large LLM deployments in production, he's always happy to help companies build their infra stacks on FM APIs or Open-source models.

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// Related Links

Website: https://portkey.ai

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Timestamps:

[00:00] Rohit's preferred coffee

[00:15] Takeaways

[03:22] Please like, share, and subscribe to our MLOps channels!

[05:16] Rohit's current work

[06:37] The Portkey landscape

[09:13] Compute unit is no longer a Cloud resource; it's a Foundational Model

[11:09] Hang-ups at high-scale models and how to combat them

[15:22] Complexity of the Apps evolving

[19:54] Rohit's working relationships with the agents

[22:52] Fine-tuning reliability

[24:38] Small language models can outperform larger ones

[26:38] Market map at Portkey

[34:37] AI Gateway

[37:59] Worker Bee and Queen Bee

[39:27] Security and Compliance

[43:11] Idea of Data Mesh

[45:57] Forward compatibility

[49:59] Decoupling AI Gateway from the code

[56:05] Hardest design decisions to make since creating Portkey

[58:52] Wrap up