Course Outline

Introduction

  • Kubeflow on GCK vs on-premise vs on other public cloud providers

Overview of Kubeflow Features on GCP

  • Declarative management of resources
  • GKE autoscaling for machine learning (ML) workloads
  • Secure connections to Jupyter
  • Persistent logs for debugging and troubleshooting
  • GPUs and TPUs to accelerate workloads

Overview of Environment Setup

  • Virtual machine preparation
  • Kubernetes cluster setup
  • Kubeflow installation

Deploying Kubeflow

  • Deploying  Kubeflow on GCP
  • Deploying Kubeflow across on-premises and cloud environments
  • Deploying Kubeflow on GKE
  • Setting up a custom domain on GKE

Pipelines on GCP

  • Setting up an end-to-end Kubeflow pipeline
  • Customizing Kubeflow Pipelines

Securing a Kubeflow Cluster

  • Setting up authentication and authorization
  • Using VPC service controls and private GKE

Storing, Accessing, Managing Data

  • Understanding shared filesystems and Network Attached Storage (NAS)
  • Using managed file storage services in GCE

Running an ML Training Job

  • Training an MNIST model

Administering Kubeflow

  • Logging and monitoring

Troubleshooting

Summary and Conclusion

Requirements

  • An understanding of machine learning concepts.
  • Knowledge of cloud computing concepts.
  • A general understanding of containers (Docker) and orchestration (Kubernetes).
  • Some Python programming experience is helpful.
  • Experience working with a command line.

Audience

  • Data science engineers.
  • DevOps engineers interesting in machine learning model deployment.
  • Infrastructure engineers interesting in machine learning model deployment.
  • Software engineers wishing to automate the integration and deployment of machine learning features with their application.
 28 Hours

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