AI/ML on Kubernetes - end to end workflow

Introduction

This describes an end to end AI/ML workflow and lifcycle. The use case is predicting customer churn - though the capabilities and tools are transferrable to any AI/ML use case.

This diagram depicts the flow and actors involved at each stage:

Workshop Setup

You or your workshop facilitator should have completed the setup as described in the Setup Instructions

Instructions

Your facilitator will have assigned you credentials that you can use throughout today's workshop. Specifically

• a username, something like user29
• a password which will be openshift unless your facilitator advises you otherwise.

Today's workshop will be split up into sub-modules - logically following the workflow in the diagram above..

• Data Engineer prepares data
• Data Scientist visualises and analyses prepared data, experiments and trains model.
• OpenShift's ML/OPs deploys the model to production

Next Steps

Let's get started. Move to Data Engineer prepares data