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Course Outline
Introduction
- Kubeflow on OpenShift vs public cloud managed services
Overview of Kubeflow on OpenShift
- Code Read Containers
- Storage options
Overview of Environment Setup
- Setting up a Kubernetes cluster
Setting up Kubeflow on OpenShift
- Installing Kubeflow
Coding the Model
- Choosing an ML algorithm
- Implementing a TensorFlow CNN model
Reading the Data
- Accessing a dataset
Kubeflow Pipelines on OpenShift
- Setting up an end-to-end Kubeflow pipeline
- Customizing Kubeflow Pipelines
Running an ML Training Job
- Training a model
Deploying the Model
- Running a trained model on OpenShift
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 (2)
The availability of the virtual desktop as form of sandbox for the participants to tinker with is great!
Benedict - Questronix Corporation
Course - OpenShift 4 for Administrators
lots of extra tools
Adam - ENIGMA SOI SP. Z O.O.
Machine Translated
