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Duration 14 hours
Course Outline
Introduction to Containerization for AI & ML
- Fundamental principles of containerization
- The suitability of containers for ML workloads
- Distinctions between containers and virtual machines
Managing Docker Images and Containers
- Comprehending images, layers, and registries
- Administering containers for ML experimentation
- Efficient utilization of the Docker CLI
Preparing ML Environments for Packaging
- Readying ML codebases for containerization
- Handling Python environments and dependencies
- Integrating CUDA and GPU support
Creating Dockerfiles for Machine Learning
- Organizing Dockerfiles for ML projects
- Best practices for performance and maintainability
- Implementation of multi-stage builds
Encapsulating ML Models and Pipelines
- Enclosing trained models within containers
- Strategies for data and storage management
- Rolling out reproducible end-to-end workflows
Operating Containerized ML Services
- Exposing API endpoints for model inference
- Scaling services utilizing Docker Compose
- Monitoring runtime behavior
Security and Compliance Factors
- Safeguarding container configurations
- Controlling access and credentials
- Protecting confidential ML assets
Deployment to Production Environments
- Releasing images to container registries
- Implementing containers in on-prem or cloud configurations
- Versioning and refreshing production services
Conclusion and Future Steps
Requirements
- A solid grasp of machine learning workflows
- Proficiency in Python or comparable programming languages
- Basic familiarity with Linux command-line operations
Target Audience
- ML engineers responsible for deploying models to production
- Data scientists focused on maintaining 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.
