NEW COURSE | 22 Hours

MLOps: Automated Pipelines and Model Monitoring

Version data with DVC, track experiments in MLflow, retrain with Prefect, containerize APIs with Docker, deploy to AWS, and monitor for drift and fairness. The full ML Ops lifecycle — from raw data to a production-grade serving endpoint.

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BLOG | 7 Min Read

What is AI Ops? Why LLM and Agent Systems Require a Different Approach

Most engineers approach their first production LLM system with instincts built for deterministic software. AI ops requires something different — from guardrails and evaluation to versioning and observability. A practical breakdown of what actually changes and why.

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NEW COURSE | 22 Hours

LLMOps: RAG Pipelines, Evaluation, and Cost Control

Operate LLM applications in production — not just prototype them. Version prompts as code, build a RAG pipeline with Chroma and OpenAI, add observability with Arize Phoenix, automate evaluation with RAGAS, control token costs, enforce guardrails, and optimize end-to-end latency.

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NEW COURSE | 22 Hours

AgentOps: Deploying, Guarding, and Monitoring AI Agents

Take an AI agent from prototype to a production system you can deploy, secure, and operate. Version agent behavior, build evaluation suites with ship/no-ship gates, deploy as a containerized CLI, enforce guardrails, add human-in-the-loop oversight, and instrument with structured logging and reasoning traces.

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TOP PICK FOR YOU | 53 Hours

Agentic AI

⭐ 4.8 (581 reviews)

Go beyond single chatbots to engineer sophisticated, coordinated teams of AI agents. This Nanodegree guides you from advanced prompting techniques ... Learn more

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