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Course Outline
Introduction to Containerization for AI & ML
- Core concepts of containerization.
- Rationale for using containers in 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.
- Efficient use 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 for ML projects.
- Best practices for performance and maintainability.
- Utilizing 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-prem or cloud setups.
- Versioning and updating production services.
Summary and Next Steps
Requirements
- A solid understanding of machine learning workflows.
- Experience programming in Python or similar languages.
- Familiarity with fundamental Linux command-line operations.
Target Audience
- ML engineers responsible for deploying models to production.
- Data scientists managing reproducible experiment 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
