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Course Outline
Basics of Containerization in MLOps
- Analyzing the requirements of the ML lifecycle
- Essential Docker concepts applicable to ML systems
- Best practices for establishing reproducible environments
Constructing Containerized ML Training Pipelines
- Bundle model training code alongside its dependencies
- Set up training jobs utilizing Docker images
- Manage datasets and artifacts within containers
Containerizing Validation and Model Assessment
- Recreate evaluation environments accurately
- Streamline validation workflows through automation
- Collect metrics and logs from containerized processes
Containerized Inference and Serving
- Architect inference microservices
- Refine runtime containers for production readiness
- Build scalable serving architectures
Orchestrating Pipelines with Docker Compose
- Synchronize multi-container ML workflows
- Handle environment isolation and configuration settings
- Incorporate supporting services such as tracking and storage
ML Model Versioning and Lifecycle Oversight
- Monitor models, images, and pipeline components
- Maintain version-controlled container environments
- Integrate tools like MLflow or equivalents
Deployment and Scaling of ML Workloads
- Execute pipelines in distributed settings
- Scale microservices via Docker-native methods
- Monitor the health of containerized ML systems
CI/CD for MLOps Using Docker
- Automate the building and deployment of ML components
- Test pipelines within containerized staging areas
- Guarantee reproducibility and facilitate rollbacks
Recap and Future Directions
Requirements
- A solid grasp of machine learning workflows
- Proficiency in Python for data analysis or model development
- Basic knowledge of container fundamentals
Intended Audience
- MLOps engineers
- DevOps specialists
- Data platform teams
21 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.
