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Course Outline
Fundamentals of Containerization in AI & ML
- Essential principles of containerization
- The suitability of containers for ML workloads
- Distinguishing features between containers and virtual machines
Managing Docker Images and Containers
- Comprehending images, layer structures, and registry systems
- Overseeing containers for ML experimentation purposes
- Efficient utilization of the Docker CLI
Encapsulating ML Environments
- Readying ML codebases for containerization
- Regulating Python environments and dependency management
- Incorporating CUDA and GPU capabilities
Crafting Dockerfiles for Machine Learning
- Organizing Dockerfiles for ML projects
- Best practices for ensuring performance and ease of maintenance
- Application of multi-stage builds
Containerization of ML Models and Pipelines
- Encapsulating trained models within containers
- Strategizing data and storage management
- Implementing reproducible end-to-end workflows
Executing Containerized ML Services
- Creating API endpoints for model inference
- Scaling services utilizing Docker Compose
- Monitoring operational behavior
Security and Compliance Factors
- Maintaining secure container configurations
- Regulating access permissions and credential management
- Safeguarding confidential ML assets
Production Environment Deployment
- Releasing images to container registries
- Implementing containers in on-premises or cloud architectures
- Version control and updating of production services
Recap and Future Steps
Requirements
- Comprehension of machine learning workflows
- Practical experience with Python or equivalent programming languages
- Proficiency with fundamental Linux command-line operations
Target Audience
- ML engineers responsible for model deployment to production
- Data scientists focused on maintaining reproducible experimental environments
- AI developers constructing scalable, containerized applications
14 Hours
Testimonials (1)
The training met expectations with its clear explanations, real-world examples, and hands-on labs that made complex topics easy to understand. It provided valuable insights into container orchestration, security, scaling and many other advanced topics.
