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Course Outline
Introduction
Key Features and Components of Kubeflow
- Containers, manifests, and related elements
The Machine Learning Pipeline Lifecycle
- Training, testing, tuning, and deployment phases
Deploying Kubeflow on a Kubernetes Cluster
- Setting up the execution environment (training clusters, production clusters, etc.)
- Downloading, installing, and customizing the platform
Executing a Machine Learning Pipeline on Kubernetes
- Creating a TensorFlow pipeline
- Building a PyTorch pipeline
Analyzing and Visualizing Results
- Exporting and visualizing key pipeline metrics
Tailoring the Execution Environment
- Adapting the stack for varied infrastructure requirements
- Upgrading existing Kubeflow deployments
Running Kubeflow on Public Clouds
- Integration with AWS, Microsoft Azure, and Google Cloud Platform
Overseeing Production Workflows
- Implementing GitOps methodologies
- Scheduling automated jobs
- Spawning Jupyter notebooks for analysis
Debugging and Troubleshooting
Summary and Wrap-up
Requirements
- Working knowledge of Python syntax
- Hands-on experience with TensorFlow, PyTorch, or other machine learning frameworks
- An active public cloud provider account (optional)
Target Audience
- Software Developers
- Data Scientists
28 Hours
