Course Outline

Introduction

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

Overview of Kubeflow Features on IBM Cloud

  • IKS
  • IBM Cloud Object Storage

Overview of Environment Setup

  • Preparing virtual machines
  • Setting up a Kubernetes cluster

Setting up Kubeflow on IBM Cloud

  • Installing Kubeflow through IKS

Coding the Model

  • Choosing an ML algorithm
  • Implementing a TensorFlow CNN model

Reading the Data

  • Accessing the MNIST dataset

Pipelines on IBM Cloud

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

Running an ML Training Job

  • Training an MNIST model

Deploying the Model

  • Running TensorFlow Serving on IKS

Integrating the Model into a Web Application

  • Creating a sample application
  • Sending prediction requests

Administering Kubeflow

  • Monitoring with Tensorboard
  • Managing logs

Securing a Kubeflow Cluster

  • Setting up authentication and authorization

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
 

Testimonials

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