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Duration 14 hours
Course Outline
Foundations of Containerization for AI & ML
- Fundamental principles of containerization
- Why containers suit ML workloads
- Distinctions between containers and virtual machines
Utilizing Docker Images and Containers
- Exploring images, layers, and registries
- Overseeing containers for ML experimentation
- Efficient use of the Docker CLI
Encapsulating ML Environments
- Readying ML codebases for containerization
- Handling Python environments and dependencies
- Incorporating CUDA and GPU capabilities
Creating Dockerfiles for Machine Learning
- Organizing Dockerfiles for ML projects
- Best practices for performance and upkeep
- Leveraging multi-stage builds
Containerizing ML Models and Pipelines
- Bundling trained models into containers
- Handling data and storage strategies
- Implementing reproducible end-to-end workflows
Operationalizing Containerized ML Services
- Creating API endpoints for model inference
- Scaling services via Docker Compose
- Tracking runtime performance
Security and Compliance Perspectives
- Securing container configurations
- Controlling access and credentials
- Protecting sensitive ML assets
Production Deployment
- Distributing images to container registries
- Deploying containers in on-prem or cloud infrastructures
- Versioning and updating production services
Conclusion and Future Directions
Requirements
- Knowledge of machine learning workflows
- Proficiency with Python or comparable programming languages
- Basic command of Linux command-line operations
Target Audience
- ML engineers responsible for production model deployment
- Data scientists maintaining reproducible experiment setups
- AI developers creating 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.