Course Outline
Foundations of Containerization in MLOps
- Understanding the requirements of the ML lifecycle
- Essential Docker concepts for ML systems
- Best practices for establishing reproducible environments
Constructing Containerized ML Training Pipelines
- Packaging model training code along with its dependencies
- Configuring training jobs via Docker images
- Managing datasets and artifacts within containers
Containerizing Validation and Model Assessment
- 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 using Docker Compose
- Coordinating multi-container ML workflows
- Managing environment isolation and configuration
- Integrating auxiliary services (e.g., tracking, storage)
ML Model Versioning and Lifecycle Management
- Tracking models, images, and pipeline components
- Maintaining version-controlled container environments
- Integrating MLflow or equivalent tools
Deployment and Scaling of ML Workloads
- Executing pipelines in distributed environments
- Scaling microservices via Docker-native methods
- Monitoring containerized ML systems
CI/CD for MLOps with Docker
- Automating the build and deployment of ML components
- Testing pipelines within containerized staging environments
- Guaranteeing reproducibility and rollback capabilities
Summary and Next Steps
Requirements
- Knowledge of machine learning workflows
- Proficiency in Python for data or model development
- Basic understanding of container fundamentals
Target Audience
- MLOps engineers
- DevOps practitioners
- Data platform teams
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 3900 € + VAT*
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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.
