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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