Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction
Overview of Kubeflow Features and Components
- Containers, manifests, and related elements.
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, installation, and customisation processes.
Running a Machine Learning Pipeline on Kubernetes
- Building a TensorFlow pipeline.
- Building a PyTorch pipeline.
Visualizing the Results
- Exporting and visualising 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
- Spawning Jupyter notebooks
Troubleshooting
Summary and Conclusion
Requirements
- Proficiency with Python syntax
- Hands-on experience with Tensorflow, PyTorch, or other machine learning frameworks
- An account with a public cloud provider (optional)
Target Audience
- Developers
- Data scientists
28 Hours
