Course Outline
Foundations of Kubeflow
- Exploring the goals and architectural design of Kubeflow
- Surveying key components and the broader ecosystem
- Examining deployment strategies and platform functionalities
Interacting with the Kubeflow Interface
- Guided navigation of the user interface
- Administration of notebooks and user workspaces
- Connecting storage solutions and external data sources
Essentials of Kubeflow Pipelines
- Understanding pipeline architecture and component structuring
- Developing pipelines using the Python SDK
- Overseeing execution, scheduling, and performance monitoring
Model Training within Kubeflow
- Implementing distributed training methodologies
- Leveraging operators such as TFJob, PyTorchJob, and others
- Controlling resources and autoscaling features in Kubernetes
Serving Models with Kubeflow
- An introduction to KFServing and KServe
- Deploying models through custom runtime environments
- Handling revisions, auto-scaling, and traffic distribution
Orchestrating ML Workflows on Kubernetes
- Managing version control for data, models, and artifacts
- Integrating CI/CD practices into ML pipelines
- Enforcing security protocols and role-based access control
Production-Grade ML Best Practices
- Architecting dependable workflow structures
- Implementing observability and continuous monitoring
- Resolving common Kubeflow operational challenges
Advanced Concepts (Optional)
- Setting up multi-tenant Kubeflow instances
- Deploying across hybrid and multi-cluster environments
- Customizing Kubeflow with bespoke components
Wrap-Up and Future Directions
Requirements
- Foundational knowledge of containerized application architectures
- Practical experience with basic command-line operations
- A working understanding of core Kubernetes concepts
Intended Audience
- Machine Learning Engineers
- Data Scientists
- DevOps teams newly adopting Kubeflow
Testimonials (4)
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How trainer deliver knowledge so effectively
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The knowledge and the patience from the trainer to answer to our questions.