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Course Outline
Foundations of Containerization for MLOps
- Assessing ML lifecycle requirements
- Essential Docker concepts for ML systems
- Best practices for establishing reproducible environments
Creating Containerized ML Training Pipelines
- Encapsulating model training code and dependencies
- Setting up training jobs using Docker images
- Handling datasets and artifacts within containers
Containerizing Validation and Model Evaluation
- Replicating evaluation environments
- Streamlining validation workflows
- Recording metrics and logs from containers
Containerized Inference and Serving
- Architecting inference microservices
- Refining runtime containers for production use
- Constructing scalable serving architectures
Orchestrating Pipelines with Docker Compose
- Managing multi-container ML workflows
- Handling environment isolation and configuration
- Integrating auxiliary services such as tracking and storage
Versioning and Lifecycle Management for ML Models
- Tracking models, images, and pipeline elements
- Maintaining version-controlled container environments
- Incorporating MLflow or equivalent tools
Deploying and Scaling ML Workloads
- Executing pipelines in distributed settings
- Expanding microservices through Docker-native methods
- Monitoring containerized ML systems
CI/CD for MLOps using Docker
- Automating the build and deployment of ML components
- Validating pipelines in containerized staging environments
- Safeguarding reproducibility and rollback capabilities
Conclusion and Future Directions
Requirements
- A solid grasp of machine learning workflows
- Practical experience with Python for data or model development
- Basic familiarity with container concepts
Audience
- MLOps engineers
- DevOps practitioners
- Data platform teams
21 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
