Course Outline

Introduction

  • Introduction to Kubernetes
  • Overview of Kubeflow Features and Architecture
  • Kubeflow on AWS vs on-premise vs on other public cloud providers

Setting up a Cluster using AWS EKS

Setting up an On-Premise Cluster using Microk8s

Deploying Kubernetes using a GitOps Approach

Data Storage Approaches

Creating a Kubeflow Pipeline

Triggering a Pipeline

Defining Output Artifacts

Storing Metadata for Datasets and Models

Hyperparameter Tuning with TensorFlow

Visualizing and Analyzing the Results

Multi-GPU Training

Creating an Inference Server for Deploying ML Models

Working with JupyterHub

Networking and Load Balancing

Auto Scaling a Kubernetes Cluster

Troubleshooting

Summary and Conclusion

Requirements

  • Familiarity with Python syntax 
  • Experience with Tensorflow, PyTorch, or other machine learning framework
  • An AWS account with necessary resources

Audience

  • Developers
  • Data scientists
  35 Hours
 

Testimonials

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