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Course Outline
Introduction
Overview of Kubeflow Features and Components
- Containers, manifests, and related artefacts.
Overview of a Machine Learning Pipeline
- Training, testing, tuning, deployment, and more.
Deploying Kubeflow to a Kubernetes Cluster
- Preparing the execution environment (including training and production clusters).
- Downloading, installing, and customising the setup.
Running a Machine Learning Pipeline on Kubernetes
- Building a TensorFlow pipeline.
- Creating a PyTorch pipeline.
Visualising the Results
- Exporting and visualising pipeline metrics.
Customising the Execution Environment
- Tailoring the stack for various 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.
- Spawning Jupyter notebooks.
Troubleshooting
Summary and Conclusion
Requirements
- A solid understanding of Python syntax
- Practical experience with Tensorflow, PyTorch, or another machine learning framework
- An account with a public cloud provider (optional)
Audience
- Developers
- Data scientists
28 Hours
