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Duration 14 hours
Course Outline
Introduction to Containerization for AI & ML
- Fundamental concepts of containerization
- The suitability of containers for ML workloads
- Primary distinctions between containers and virtual machines
Managing Docker Images and Containers
- Comprehending images, layers, and registries
- Overseeing containers for ML experimentation
- Efficiently utilizing the Docker CLI
Packaging ML Environments
- Preparing ML codebases for containerization
- Managing Python environments and dependencies
- Integrating CUDA and GPU support
Creating Dockerfiles for Machine Learning
- Structuring Dockerfiles for ML projects
- Best practices for performance and maintainability
- Leveraging multi-stage builds
Containerizing ML Models and Pipelines
- Encapsulating trained models into containers
- Managing data and storage strategies
- Deploying reproducible end-to-end workflows
Running Containerized ML Services
- Exposing API endpoints for model inference
- Scaling services using Docker Compose
- Monitoring runtime behavior
Security and Compliance Considerations
- Securing container configurations
- Managing access and credentials
- Handling confidential ML assets
Deployment to Production Environments
- Publishing images to container registries
- Deploying containers in on-premises or cloud configurations
- Versioning and updating production services
Summary and Next Steps
Requirements
- Knowledge of machine learning workflows
- Proficiency in Python or similar programming languages
- Basic familiarity with Linux command-line operations
Target Audience
- ML engineers deploying models to production
- Data scientists managing reproducible experiment environments
- AI developers building 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.
