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Course Outline
Introduction
Overview of Kubeflow Features and Components
- Containers, manifests, etc.
Overview of a Machine Learning Pipeline
- Training, testing, tuning, deploying, etc.
Deploying Kubeflow to a Kubernetes Cluster
- Preparing the execution environment (training cluster, production cluster, etc.)
- Downloading, installing, and customizing.
Executing a Machine Learning Pipeline on Kubernetes
- Building a TensorFlow pipeline.
- Building a PyTorch pipeline.
Visualizing the Results
- Exporting and visualizing pipeline metrics
Customizing the Execution Environment
- Adapting the stack for diverse infrastructures
- Upgrading a Kubeflow deployment
Running Kubeflow on Public Clouds
- AWS, Microsoft Azure, Google Cloud Platform
Managing Production Workflows
- Implementing GitOps methodology
- Scheduling jobs
- Spawning Jupyter notebooks
Troubleshooting
Summary and Conclusion
Requirements
- Basic familiarity with Python syntax
- Practical experience with Tensorflow, PyTorch, or other machine learning frameworks
- An account with a public cloud provider (optional)
Target Audience
- Developers
- Data scientists
28 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 6400 € + VAT*
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