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Duration 21 hours
Course Outline
Foundations of Containerization for MLOps
- Analyzing ML lifecycle requirements.
- Essential Docker concepts applicable to ML systems.
- Best practices for establishing reproducible environments.
Constructing Containerized ML Training Pipelines
- Packaging model training code along with its dependencies.
- Configuring training jobs utilizing Docker images.
- Handling datasets and artifacts within containers.
Containerizing Validation and Model Evaluation
- Recreating consistent evaluation environments.
- Automating validation workflows.
- Capturing metrics and logs generated by containers.
Containerized Inference and Serving
- Architecting 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 auxiliary services such as tracking and storage.
ML Model Versioning and Lifecycle Management
- Tracking models, images, and various pipeline components.
- Maintaining version-controlled container environments.
- Integrating MLflow or comparable tools.
Deploying and Scaling ML Workloads
- Executing pipelines across distributed environments.
- Scaling microservices using Docker-native methods.
- Monitoring containerized ML systems.
CI/CD for MLOps with Docker
- Automating the build and deployment processes for ML components.
- Testing pipelines within containerized staging environments.
- Safeguarding reproducibility and managing rollbacks.
Summary and Next Steps
Requirements
- A solid understanding of machine learning workflows.
- Proficiency in Python for data or model development.
- Familiarity with the core principles of containerization.
Target Audience
- MLOps engineers.
- DevOps practitioners.
- Data platform teams.
Testimonials (2)
As i said before , for a person like me (no exp. ) this was a gateway to understanding features and functions with these programs/tools & etc. .
Patrick V. Duylovski - UBB + DZI (KBC GROUP)
Course - Docker and Kubernetes
multi-tiered, structured course programme.
