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Duration 28 hours
Course Outline
Introduction
Exploration of Kubeflow Features and Components
- Containers, manifests, and related concepts.
Understanding a Machine Learning Pipeline
- Stages including training, testing, tuning, and deployment.
Deploying Kubeflow onto a Kubernetes Cluster
- Setting up the execution environment (e.g., training cluster, production cluster).
- Process of downloading, installing, and customizing.
Executing a Machine Learning Pipeline on Kubernetes
- Creating a TensorFlow pipeline.
- Creating a PyTorch pipeline.
Visualizing Outcomes
- Exporting and interpreting pipeline metrics
Tailoring the Execution Environment
- Adapting the stack for varied infrastructure requirements
- Updating a Kubeflow deployment
Operating Kubeflow on Public Clouds
- Covering AWS, Microsoft Azure, and Google Cloud Platform
Overseeing Production Workflows
- Implementing GitOps methodology
- Managing job scheduling
- Launching Jupyter notebooks
Troubleshooting
Recap and Final Remarks
Requirements
- Basic understanding of Python syntax
- Practical experience with TensorFlow, PyTorch, or another machine learning framework
- An account with a public cloud provider (optional)
Target Audience
- Developers
- Data scientists
