This course is for developers who create and run machine learning applications on HPE Ezmeral Container Platform 5.3. The course teaches how to deploy clusters and provide real-life prediction analysis for specific use cases. The course consists of 30% lecture and 70% lab exercises.

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• AI/ML application administration

experience (Spark, Jupyter Notebook,

Tensorflow, etc.)

• Experience in machine learning lifecycle

(e.g. model training/development and

model deployment)

• Bash/shell/python scripting

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Machine Learning Ops Overview

• Creating an ML Ops tenant

• External authentication

• Project repository

• Source control

• Model registr

• Training

• Deployments

• Data sources

• App store

• Notebooks HPE

Personas Overview

• Platform administrator (site administrator)

• Project administrator

• Project member

Project Repository Setup

• Initial access to HPE Ezmeral Container Platform

• Setting up ML Ops environment and project repository

• ML Ops clusters

Training Cluster Setup

• Creating a training cluster

• Training cluster configurations

• Training cluster

• Spark training

• Accessing Python training cluster outside of HPE

Ezmeral Container Platform

• General notes on training clusters Notebook Setup

• Creating a notebook cluster

• Notebook cluster configuration

• More details on notebooks on ML Ops

• Create notebook with training cluster

• Review

• Training first model

Model Registry and Deployment

• Model registry

• Model registry configurations

• More details on model registry

• Deployments (Method 1)

• Deployments (Method 2)

• Deployments clusters

• Register and deploy the model Inference

• “Ready” deployment cluster

• Doing inference

• Walkthrough of scoring script

• Local notebook to ML Ops training cluster

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Dates & Locations

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