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Course Outline
Foundations of Containerization for AI and ML
- Fundamental concepts of containerization
- The advantages of containers for ML workloads
- Distinctions between containers and virtual machines
Managing Docker Images and Containers
- Concepts of images, layers, and registries
- Container management for ML experimentation
- Efficient utilization of the Docker CLI
Packaging ML Environments
- Preparing ML codebases for containerization
- Managing Python environments and dependencies
- Incorporating CUDA and GPU support
Creating Dockerfiles for Machine Learning
- Structuring Dockerfiles for ML projects
- Best practices for ensuring performance and maintainability
- Utilizing multi-stage builds
Containerizing ML Models and Pipelines
- Encapsulating trained models within containers
- Strategies for data and storage management
- Implementing reproducible end-to-end workflows
Running Containerized ML Services
- Exposing API endpoints for model inference
- Scaling services via Docker Compose
- Monitoring runtime performance
Security and Compliance
- Securing container configurations
- Managing access controls and credentials
- Protecting confidential ML assets
Production Deployment
- Publishing images to container registries
- Deploying containers in on-premises or cloud configurations
- Versioning and updating production services
Summary and Future Directions
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 into production
- Data scientists seeking to manage reproducible experimental environments
- AI developers creating scalable, containerized applications
14 Hours
Testimonials (3)
How trainer deliver knowledge so effectively
Vu Thoai Le - Reply Polska sp. z o. o.
Course - Certified Kubernetes Administrator (CKA) - exam preparation
the trainer had a lot of knowledge and patience to share with us
Bogdan Olaru
Course - Introduction to Docker
The knowledge and exchanges with Augustin