Arize University

Learn to build trustworthy models no matter where you are on your machine learning observability journey.

Arize University: Certifications

Get certified in LLM evaluation, LLM guardrails, LLM observability, and more!

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Self-Guided Learning (LLMOps)

From the five pillars of LLM observability to everything you need to build and benchmark your LLM application

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Self-Guided Learning (ML/CV)

A wealth of educational content covering the fundamentals and advanced topics for effective machine learning observability.

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Blog / Resources

Search the depths of our content including blogs, papers, case studies, tutorials and videos.

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Community events

Join us for a range of virtual learning events including bi-weely AI research paper readings.

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Slack Community

Learn from AI engineers, data scientists, and AI researchers who are building LLM applications

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Useful Educational Content on AI Observability

Dive into the fundamentals of troubleshooting AI in production with these 101-style primers on key concepts

LLMOps

Core concepts and emerging best practices for large language model operations (LLMOps), from prompt engineering to LLM agents and observability.

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ML Observability

An overview of ML observability fundamentals, the four pillars of ML observability, its implementation in the ML toolchain, and common techniques.

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Model Drift

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.

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