Course Outline
Foundations of Containerization in MLOps
- Analyzing the requirements of the ML lifecycle.
- Essential Docker concepts applicable to ML systems.
- Best practices for establishing reproducible environments.
Constructing Containerized ML Training Pipelines
- Packaging model training code and its dependencies.
- Setting up training jobs via Docker images.
- Handling datasets and artifacts within containers.
Containerizing Validation and Model Evaluation
- Replicating evaluation environments accurately.
- Streamlining validation workflows through automation.
- Recording metrics and logs from container instances.
Containerized Inference and Serving
- Architecting inference microservices.
- Optimizing runtime containers for production stability.
- Implementing scalable serving architectures.
Orchestrating Pipelines with Docker Compose
- Coordinating workflows across multiple containers.
- Managing environment isolation and configuration.
- Integrating auxiliary services such as tracking and storage.
ML Model Versioning and Lifecycle Management
- Tracking models, images, and pipeline elements.
- Maintaining version-controlled container environments.
- Integrating tools like MLflow or similar alternatives.
Deploying and Scaling ML Workloads
- Executing pipelines within distributed environments.
- Scaling microservices using native Docker capabilities.
- Monitoring the health of containerized ML systems.
Implementing CI/CD for MLOps with Docker
- Automating the build and deployment of ML components.
- Testing pipelines in containerized staging setups.
- Guaranteeing reproducibility and rollback capabilities.
Summary and Subsequent Steps
Requirements
- A solid grasp of machine learning workflows.
- Proficiency in Python for data analysis or model development.
- Basic knowledge 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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How trainer deliver knowledge so effectively
Vu Thoai Le - Reply Polska sp. z o. o.
Course - Certified Kubernetes Administrator (CKA) - exam preparation
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Course - Introduction to Docker
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