Phase 7: AI Evaluation & Observability · 55 min · Python · GitHub Actions · LangSmith

CI/CD for AI Deployments

Every PR gets a preview. Every preview gets evals. Every eval gate blocks.

What you will learn

Build CI pipelines for AI: lint, unit tests, integration tests, evaluation suite, quality gate
Set up preview deployments: every PR gets a preview with its own eval results
Configure quality gates: minimum eval scores, no regressions beyond threshold, human review for high-risk
Implement CD patterns: blue-green, canary with eval monitoring, automatic rollback on regression
Integrate PromptHub: versioning prompts, webhook-triggered pipelines on prompt changes

Objective

What you'll learn

This lesson takes approximately 55 min. By the end, you will be able to:

Build CI pipelines for AI: lint, unit tests, integration tests, evaluation suite, quality gate
Set up preview deployments: every PR gets a preview with its own eval results
Configure quality gates: minimum eval scores, no regressions beyond threshold, human review for high-risk
Implement CD patterns: blue-green, canary with eval monitoring, automatic rollback on regression
Integrate PromptHub: versioning prompts, webhook-triggered pipelines on prompt changes

The Problem

Every pull request gets a preview deployment. Every preview gets an evaluation run. Every evaluation gate blocks the merge if metrics drop below threshold. This is CI/CD for AI: the pipeline that prevents regressions from reaching production. This lesson covers eval-gated merges, canary deployments with metric-based rollback, and CI configurations for AI workflows.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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