How to Systematically Test and Evaluate Your LLMs Apps // Gideon Mendels // #269

MLOps.community

Gideon Mendels is the Chief Executive Officer at Comet, the leading solution for managing machine learning workflows. How to Systematically Test and Evaluate Your LLMs Apps

// MLOps Podcast #269 with Gideon Mendels, CEO of Comet.

// Abstract

When building LLM Applications, Developers need to take a hybrid approach from both ML and SW Engineering best practices. They need to define eval metrics and track their entire experimentation to see what is and is not working. They also need to define comprehensive unit tests for their particular use case so they can confidently check if their LLM App is ready to be deployed.

// Bio

Gideon Mendels is the CEO and co-founder of Comet, the leading solution for managing machine learning workflows from experimentation to production. He is a computer scientist, ML researcher and entrepreneur at his core. Before Comet, Gideon co-founded GroupWize, where they trained and deployed NLP models processing billions of chats. His journey with NLP and Speech Recognition models began at Columbia University and Google, where he worked on hate speech and deception detection.

// MLOps Swag/Merch

https://mlops-community.myshopify.com/

// Related Links

Website: https://www.comet.com/site/

All the Hard Stuff with LLMs in Product Development // Phillip Carter // MLOps Podcast #170: https://youtu.be/DZgXln3v85s

Opik by Comet: https://www.comet.com/site/products/opik/

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Timestamps:

[00:00] Gideon's preferred coffee

[00:17] Takeaways

[01:50] A huge shout-out to Comet ML for sponsoring this episode!

[02:09] Please like, share, leave a review, and subscribe to our MLOps channels!

[03:30] Evaluation metrics in AI

[06:55] LLM Evaluation in Practice

[10:57] LLM testing methodologies

[16:56] LLM as a judge

[18:53] OPIC track function overview

[20:33] Tracking user response value

[26:32] Exploring AI metrics integration

[29:05] Experiment tracking and LLMs

[34:27] Micro Macro collaboration in AI

[38:20] RAG Pipeline Reproducibility Snapshot

[40:15] Collaborative experiment tracking

[45:29] Feature flags in CI/CD

[48:55] Labeling challenges and solutions

[54:31] LLM output quality alerts

[56:32] Anomaly detection in model outputs

[1:01:07] Wrap up