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AI quality and MLOps guides
Learn about AI observability, ML and LLM evaluation and MLOps with our in-depth guides.
aPPLIED course
LLM evaluations for AI builders
Sign up for our free video course with 10 hands-on code tutorials. From designing custom LLM judges to RAG evaluations and adversarial testing. If you’re working hands-on with LLMs and have basic Python skills, this course is for you.
LLM GUIDE
A complete guide to LLM quality
How to evaluate the quality of generative outputs and LLM-based systems? In this guide, we break down different evaluation methods and metrics for AI-powered products.
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course
LLM evaluations for AI product teams
Building an LLM-powered product? Sign up for our free course on LLM evaluations for AI product teams. A gentle introduction to evaluating LLM-powered apps, no coding knowledge required.
MACHINE LEARNING IN PRODUCTION
A complete guide to ML in production
How to maintain ML models once after you deploy them, and what exactly to prepare for? In this guide, we look into the key concepts that relate to production ML model operations.
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course
Open-source ML observability course
Want to learn production ML monitoring? Sign up for our Open-source ML observability course for data scientists and ML engineers. It's free!
Ranking and recommendations metrics in ml
A complete guide to ranking quality metrics
How do you evaluate the quality of ranking and recommender systems? In this guide, we look into the key ranking metrics and explain them step by step.
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ML Monitoring metrics
ML monitoring metrics in production
"ML monitoring" can mean many things. Are you tracking service latency? Model accuracy? Data quality? This guide organizes everything one can look at in a single framework.
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Classification metrics in ml
A complete guide to classification metrics in ML
How to evaluate the quality of a classification model? In this guide, we break down different machine learning metrics for binary and multi-class problems.
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DATA Drift detection
5 methods to detect data drift on large datasets
We ran an experiment to help build an intuition on how popular drift detection methods behave. In this guide, we share the key takeaways and the code to run the tests on your data.
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5 methods to detect drift in ML embeddings
Monitoring embedding drift is relevant for the production use of LLM and NLP models. We ran experiments to compare 5 drift detection methods. Here is what we found.
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