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Course Outline
Foundations of Containerization for MLOps
- Understanding the requirements of the ML lifecycle.
- Key Docker concepts essential for ML systems.
- Best practices for establishing reproducible environments.
Building Containerized ML Training Pipelines
- Packaging model training code along with its dependencies.
- Configuring training jobs using Docker images.
- Managing datasets and artifacts within containers.
Containerizing Validation and Model Evaluation
- Recreating 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.
- Managing environment isolation and configuration.
- Integrating supporting services (e.g., tracking, storage).
ML Model Versioning and Lifecycle Management
- Tracking models, images, and pipeline components.
- Utilizing version-controlled container environments.
- Integrating MLflow or comparable 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 the builds and deployment of ML components.
- Testing pipelines within containerized staging environments.
- Ensuring reproducibility and facilitating rollbacks.
Summary and Next Steps
Requirements
- A foundational understanding of machine learning workflows.
- Experience with Python for data processing or model development.
- Familiarity with the core concepts of containers.
Audience
- MLOps engineers.
- DevOps practitioners.
- Data platform teams.
21 Hours
Testimonials (1)
The trainer's broad knowledge, his abilities to solve issues that spontaneously occurred during the practice sessions. Also, the exercises themselves are adequate to help fix the subjects contained in the course.
