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Duration 14 hours
Course Outline
Introduction to Containerization for AI & ML
- Foundational concepts of containerization
- The suitability of containers for ML workloads
- Distinctions between containers and virtual machines
Utilizing Docker Images and Containers
- Comprehending images, layers, and registries
- Managing containers for ML experimentation
- Efficient use of the Docker CLI
Packaging ML Environments
- Preparing ML codebases for containerization
- Handling Python environments and dependencies
- Integrating CUDA and GPU support
Creating Dockerfiles for Machine Learning
- Structuring Dockerfiles for ML projects
- Best practices for enhancing performance and maintainability
- Implementing multi-stage builds
Containerizing ML Models and Pipelines
- Packaging trained models into containers
- Implementing data and storage strategies
- Deploying reproducible end-to-end workflows
Operating Containerized ML Services
- Exposing API endpoints for model inference
- Scaling services using Docker Compose
- Monitoring runtime behavior
Security and Compliance Considerations
- Ensuring secure container configurations
- Managing access controls and credentials
- Protecting confidential ML assets
Production Environment Deployment
- Publishing images to container registries
- Deploying containers in on-premise or cloud configurations
- Versioning and updating production services
Summary and Future Steps
Requirements
- A solid understanding of machine learning workflows
- Experience with Python or comparable programming languages
- Familiarity with fundamental Linux command-line operations
Target Audience
- ML engineers responsible for deploying models to production
- Data scientists managing reproducible experiment environments
- AI developers constructing scalable containerized applications
Testimonials (2)
As i said before , for a person like me (no exp. ) this was a gateway to understanding features and functions with these programs/tools & etc. .
Patrick V. Duylovski - UBB + DZI (KBC GROUP)
Course - Docker and Kubernetes
multi-tiered, structured course programme.
