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Course Outline
Introduction to CI/CD for AI Workflows
- Unique challenges of AI model delivery pipelines
- Comparing traditional DevOps and MLOps processes
- Core components of automated model deployment
Containerizing AI Models with Docker
- Designing efficient Dockerfiles for ML inference
- Managing dependencies and model artifacts
- Building secure and optimized images
Setting Up CI/CD Pipelines
- CI/CD tooling options and their ecosystems
- Building pipelines for automated model packaging
- Validating pipelines with automated checks
Testing AI Models in CI
- Automating data integrity checks
- Unit and integration tests for model services
- Performance and regression validation
Automated Deployment of Docker-Based AI Services
- Deploying AI containers to cloud environments
- Implementing blue-green and canary rollouts
- Rollback strategies for failed deployments
Managing Model Versions and Artifacts
- Using registries for model and container version control
- Tagging, signing, and promoting images
- Coordinating model updates across services
Monitoring and Observability in CI/CD for AI
- Tracking pipeline and model performance
- Alerting for failed builds or model drift
- Tracing inference behavior across environments
Scaling CI/CD Pipelines for AI Systems
- Parallelizing builds for large models
- Optimizing compute and storage resources
- Integrating distributed and remote runners
Summary and Next Steps
Requirements
- An understanding of machine learning model lifecycles
- Experience with Docker containerization
- Familiarity with CI/CD concepts and pipelines
Audience
- DevOps engineers
- MLOps teams
- AI-ops engineers
21 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customised Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from £4800 + VAT*
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