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879Testing Philosophy for AI Systems
880Unit Testing Prompt Templates
881Testing LLM API Calls with Mocks
882Testing Embedding Generation
883Testing Retrieval Logic
884Testing Output Parsers and Validation
885Integration Testing RAG Pipelines
886Testing Agent Tool Execution
887Testing with Deterministic LLMs
888Testing Error Handling and Retries
889Property-Based Testing for AI Components
890Test Coverage and Fixtures for AI Systems
891What is End-to-End Testing for AI Systems
892Setting Up E2E Test Environments
893Testing Complete RAG Pipelines
894Testing Agent Workflows End-to-End
895Introduction to Snapshot Testing
896Creating and Managing Snapshots
897Snapshot Testing for Prompt Changes
898Handling Non-Deterministic Outputs
899Performance and Latency Testing
900E2E Test Data Management and Fixtures
901CI/CD Basics for AI Systems
902Version Control for AI Artifacts
903GitHub Actions for AI Pipelines
904CI Environment Setup and Secrets
905Automated Prompt and RAG Testing
906Model Registry Integration
907Regression Detection in CI
908Cost Gates and Budget Limits
909Parallel Testing and Matrix Builds
910CI Monitoring and Debugging Failures
911Model Versioning Fundamentals
912Semantic Versioning for AI Components
913Git-Based Versioning for Prompts and Code
914Model Registries and Artifact Management
915Blue-Green Deployments for AI Systems
916Canary Releases and Progressive Rollouts
917Shadow Deployments for Safe Testing
918Rollback Strategies and Circuit Breakers
919Configuration Management and Feature Flags
920Deployment Pipelines and Approval Gates

879Testing Philosophy for AI Systems
880Unit Testing Prompt Templates
881Testing LLM API Calls with Mocks
882Testing Embedding Generation
883Testing Retrieval Logic
884Testing Output Parsers and Validation
885Integration Testing RAG Pipelines
886Testing Agent Tool Execution
887Testing with Deterministic LLMs
888Testing Error Handling and Retries
889Property-Based Testing for AI Components
890Test Coverage and Fixtures for AI Systems
891What is End-to-End Testing for AI Systems
892Setting Up E2E Test Environments
893Testing Complete RAG Pipelines
894Testing Agent Workflows End-to-End
895Introduction to Snapshot Testing
896Creating and Managing Snapshots
897Snapshot Testing for Prompt Changes
898Handling Non-Deterministic Outputs
899Performance and Latency Testing
900E2E Test Data Management and Fixtures
901CI/CD Basics for AI Systems
902Version Control for AI Artifacts
903GitHub Actions for AI Pipelines
904CI Environment Setup and Secrets
905Automated Prompt and RAG Testing
906Model Registry Integration
907Regression Detection in CI
908Cost Gates and Budget Limits
909Parallel Testing and Matrix Builds
910CI Monitoring and Debugging Failures
911Model Versioning Fundamentals
912Semantic Versioning for AI Components
913Git-Based Versioning for Prompts and Code
914Model Registries and Artifact Management
915Blue-Green Deployments for AI Systems
916Canary Releases and Progressive Rollouts
917Shadow Deployments for Safe Testing
918Rollback Strategies and Circuit Breakers
919Configuration Management and Feature Flags
920Deployment Pipelines and Approval Gates

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Lesson 914 of 1,886·22. Testing and CI/CD for AI SystemsPro lesson

Model Registries and Artifact Management

Use model registries like MLflow or Weights & Biases to track, version, and manage model artifacts centrally.

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