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Course Outline
Introduction
Overview of Kubeflow Features and Components
- Containers, manifests, and related elements.
Overview of a Machine Learning Pipeline
- Phases including training, testing, tuning, and deployment.
Deploying Kubeflow to a Kubernetes Cluster
- Preparing the execution environment (e.g., training cluster, production cluster).
- Downloading, installing, and customizing the setup.
Executing a Machine Learning Pipeline on Kubernetes
- Building a TensorFlow pipeline.
- Constructing a PyTorch pipeline.
Visualizing the Results
- Exporting and visualizing pipeline metrics.
Customizing the Execution Environment
- Tailoring the stack for diverse infrastructures.
- Upgrading a Kubeflow deployment.
Running Kubeflow on Public Clouds
- AWS, Microsoft Azure, and Google Cloud Platform.
Managing Production Workflows
- Implementing GitOps methodology.
- Scheduling jobs.
- Generating Jupyter notebooks.
Troubleshooting
Summary and Conclusion
Requirements
- Basic proficiency in Python syntax
- Practical experience with TensorFlow, PyTorch, or other machine learning frameworks
- An account with a public cloud provider (optional)
Target Audience
- Software Developers
- Data Scientists
28 Hours
