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Course Outline
Introduction to Containerization for AI & ML
- Core concepts of containerization
- Reasons why containers are ideal for ML workloads
- Key distinctions between containers and virtual machines
Working with Docker Images and Containers
- Understanding images, layers, and registries
- Managing containers for ML experimentation
- Utilizing the Docker CLI efficiently
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 for ML projects
- Best practices for performance and maintainability
- Leveraging multi-stage builds
Containerizing ML Models and Pipelines
- Packaging 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 with Docker Compose
- Monitoring runtime behavior
Security and Compliance Considerations
- Ensuring secure container configurations
- Managing access and credentials
- Handling 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 understanding of machine learning workflows
- Practical experience with Python or similar programming languages
- Familiarity with fundamental Linux command-line operations
Target Audience
- ML engineers responsible for deploying models to production
- Data scientists managing reproducible experimental environments
- AI developers constructing scalable containerized applications
14 Hours
Testimonials (2)
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