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Course Outline
Foundations of Containerization for MLOps
- Understanding the requirements of the ML lifecycle.
- Key Docker concepts relevant to ML systems.
- Best practices for establishing reproducible environments.
Building Containerized ML Training Pipelines
- Packaging model training code alongside its dependencies.
- Configuring training jobs using Docker images.
- Managing datasets and artifacts within containers.
Containerizing Validation and Model Evaluation
- Reproducing evaluation environments accurately.
- 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.
- Maintaining version-controlled container environments.
- Integrating tools like MLflow or similar solutions.
Deploying and Scaling ML Workloads
- Executing 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 enabling rollbacks.
Summary and Next Steps
Requirements
- A solid understanding of machine learning workflows.
- Experience using Python for data processing or model development.
- Familiarity with the fundamentals of containerization.
Target 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
