HPE Ezmeral ML OPs

System developers, big data application developers, business analysts, data scientists, data engineers.

During this course, you will learn how to:

• Set up the project repository

• Create a training cluster

• Create a Jupyter notebook and attach it to a

training cluster

• Run through an example of a typical machine

learning workflow

• Operationalize your model

• Make a prediction (inference)

• Obtain in-depth knowledge of HPE Ezmeral

Container Platform 5.3 ML Ops

• Apply best practices to help accelerate

the development of user-based prediction

analysis

HJ7H2S (hpe.com)

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

Lab 1: Initial Access to HPE Ezmeral Container

Platform • Task 1: Initial log-on to HPE Ezmeral Container Platform

Management Console

• Task 2: Lab system setup

• Task 3: Initial log-on to controller

Lab 2: Setting Up ML Ops Environment and

Project Repository • Task 1: Set up the ML Ops environment

• Task 2: Install and register app from App Catalog

• Task 3: Setup the project repository

Lab 3: Create Training Clusters • Task 1: Create training cluster

Lab 4: Create Notebooks with Training Cluster • Task 1: Create notebook with training cluster

Lab 5: Training First Model • Task 1: Login to Jupyter hub • Task 2: Training the model

Lab 6: Register and Deploy the Model • Task 1: Register the model • Task 2: Deploy the model

Lab 7: Inference • Task 1: Generate prediction requests

Lab 8: Local Notebook to ML Ops Training Cluster • Task 1: Making required file configurations

• Task 2: Accessing training cluster through Jupyter Notebook

• Task 3: Training the model through local notebook

Lab 9: Spark Deployment • Task 1: Setup Spark deployment environment

• Task 2: Stopping cluster in AIML tenant

• Task 3: Create Spark training cluster

• Task 4: Create Spark notebook cluster

• Task 5: Train the used car pricing model

• Task 6: Register new model

• Task 7: Deploy the model

• Task 8: Inference

• 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 scriptin