Machine Learning Glossary
Common terms in data science and machine learning demystified for ML practitioners.
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Core concepts and emerging best practices for large language model operations (LLMOps), from prompt engineering to LLM agents and observability.
Read more →An overview of ML observability fundamentals, the four pillars of ML observability, its implementation in the ML toolchain, and common techniques.
Read more →Learn what constitutes model drift, how to monitor for drift in machine learning models, the types of drift — including concept drift, feature drift, and upstream drift — and drift resolution techniques.
Read more →Common terms in data science and machine learning demystified for ML practitioners.