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Course Outline
Foundations of Containerization for MLOps
- Understanding ML lifecycle requirements.
- Key Docker concepts for ML systems.
- Best practices for creating reproducible environments.
Building Containerized ML Training Pipelines
- Packaging model training code and dependencies.
- Configuring training jobs using Docker images.
- Managing datasets and artifacts within containers.
Containerizing Validation and Model Evaluation
- Reproducing evaluation environments.
- Automating validation workflows.
- Capturing metrics and logs from containers.
Containerized Inference and Serving
- Designing inference microservices.
- Optimizing runtime containers for production use.
- Implementing scalable serving architectures.
Pipeline Orchestration with Docker Compose
- Coordinating multi-container ML workflows.
- Environment isolation and configuration management.
- Integrating supporting services (e.g., tracking, storage).
ML Model Versioning and Lifecycle Management
- Tracking models, images, and pipeline components.
- Version-controlled container environments.
- Integrating MLflow or similar tools.
Deploying and Scaling ML Workloads
- Running pipelines in distributed environments.
- Scaling microservices using Docker-native approaches.
- Monitoring containerized ML systems.
CI/CD for MLOps with Docker
- Automating builds and deployment of ML components.
- Testing pipelines in containerized staging environments.
- Ensuring reproducibility and rollbacks.
Summary and Next Steps
Requirements
- A foundational understanding of machine learning workflows.
- Experience with Python for data processing or model development.
- Familiarity with container fundamentals.
Audience
- MLOps engineers
- DevOps practitioners
- Data platform teams
21 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
