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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
- Distinguishing features between containers and virtual machines
Working with Docker Images and Containers
- Comprehending images, layers, and registries
- Managing containers for ML experimentation
- Efficient utilization of the Docker CLI
Packaging ML Environments
- Preparing ML codebases for containerization
- Managing Python environments and dependencies
- Integrating CUDA and GPU support
Building Dockerfiles for Machine Learning
- Structuring Dockerfiles effectively for ML projects
- Best practices for enhancing performance and maintainability
- Implementing multi-stage builds
Containerizing ML Models and Pipelines
- Encapsulating trained models within containers
- Strategizing data and storage management
- Deploying reproducible end-to-end workflows
Running Containerized ML Services
- Exposing API endpoints for model inference
- Scaling services utilizing Docker Compose
- Monitoring runtime behavior
Security and Compliance Considerations
- Securing container configurations
- Managing access controls and credentials
- Safeguarding confidential ML assets
Deploying to Production Environments
- Publishing images to container registries
- Deploying containers in on-premises or cloud setups
- Versioning and updating production services
Summary and Next Steps
Requirements
- A solid grasp of machine learning workflows
- Proficiency in Python or similar programming languages
- Basic familiarity with Linux command-line operations
Target Audience
- ML engineers responsible for deploying models to production
- Data scientists overseeing reproducible experimental 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.
