CI/CD for AI: Automating Docker-Based Model Builds and Deployments Training Course
CI/CD for AI represents a systematic method for automating the packaging, testing, containerization, and deployment of AI models through continuous integration and delivery pipelines.
This instructor-led training session, available both online and onsite, is designed for professionals with an intermediate level of expertise who aim to streamline end-to-end AI model delivery workflows using Docker and CI/CD platforms.
Upon completing this course, participants will be equipped to:
- Develop automated pipelines for constructing and evaluating AI model containers.
- Establish version control and ensure reproducibility throughout the model lifecycle.
- Incorporate automated deployment strategies for AI services.
- Apply CI/CD best practices specifically adapted for machine learning operations.
Course Format
- Instructor-led presentations coupled with technical discussions.
- Hands-on labs and practical implementation exercises.
- Realistic simulations of CI/CD workflows within a controlled environment.
Customization Options
- If your organization requires tailored pipeline workflows or specific platform integrations, please reach out to customize this course for your needs.
Course Outline
Introduction to CI/CD for AI Workflows
- Exploring the unique challenges associated with AI model delivery pipelines
- Contrasting traditional DevOps processes with MLOps methodologies
- Identifying core components of automated model deployment
Containerizing AI Models with Docker
- Designing efficient Dockerfiles tailored for ML inference
- Managing dependencies and model artifacts effectively
- Constructing secure and optimized images
Setting Up CI/CD Pipelines
- Overview of CI/CD tooling options and their respective ecosystems
- Constructing pipelines for automated model packaging
- Validating pipeline functionality through automated checks
Testing AI Models in CI
- Automating data integrity verification
- Conducting unit and integration tests for model services
- Performing performance assessments and regression validation
Automated Deployment of Docker-Based AI Services
- Deploying AI containers to cloud environments
- Implementing blue-green and canary rollout strategies
- Establishing rollback procedures for unsuccessful deployments
Managing Model Versions and Artifacts
- Leveraging registries for model and container version control
- Executing tagging, signing, and promotion of images
- Coordinating model updates across various services
Monitoring and Observability in CI/CD for AI
- Tracking pipeline status and model performance metrics
- Configuring alerts for failed builds or model drift
- Tracing inference behavior across different environments
Scaling CI/CD Pipelines for AI Systems
- Parallelizing builds to accommodate large models
- Optimizing compute and storage resource utilization
- Integrating distributed and remote runners
Summary and Next Steps
Requirements
- A foundational understanding of machine learning model lifecycles
- Practical experience with Docker containerization
- Familiarity with CI/CD concepts and pipeline structures
Target Audience
- DevOps engineers
- MLOps teams
- AI-ops engineers
Open Training Courses require 5+ participants.
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