Dziękujemy za wysłanie zapytania! Jeden z członków naszego zespołu skontaktuje się z Państwem wkrótce.
Dziękujemy za wysłanie rezerwacji! Jeden z członków naszego zespołu skontaktuje się z Państwem wkrótce.
Plan Szkolenia
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.
Running a Machine Learning Pipeline on Kubernetes
- Building a TensorFlow pipeline.
- Building a PyTorch pipleline.
Visualizing the Results
- Exporting and visualizing pipeline metrics
Customizing the Execution Environment
- Customizing the stack for diverse infrastructures
- Upgrading a Kubeflow deployment
Running Kubeflow on Public Clouds
- AWS, Microsoft Azure, Google Cloud Platform
Managing Production Workflows
- Running with GitOps methodology
- Scheduling jobs
- Spawning Jupyter notebooks
Troubleshooting
Summary and Conclusion
Wymagania
- Familiarity with Python syntax
- Experience with Tensorflow, PyTorch, or other machine learning framework
- A public cloud provider account (optional)
Audience
- Developers
- Data scientists
28 godzin
