HPE_HJ7H2S HPE Ezmeral ML OPs

COURSE OBJECTIVE:
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 atraining cluster• Run through an example of a typical machinelearning workflow• Operationalize your model• Make a prediction (inference)• Obtain in-depth knowledge of HPE EzmeralContainer Platform 5.3 ML Ops• Apply best practices to help acceleratethe development of user-based predictionanalysis

TARGET AUDIENCE:
System developers, big data application developers, business analysts, data scientists, data engineers.

COURSE PREREQUISITES:
• AI/ML application administrationexperience (Spark, Jupyter Notebook,Tensorflow, etc.) • Experience in machine learning lifecycle(e.g. model training/development andmodel deployment) • Bash/shell/python scriptin

COURSE CONTENT:
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 HPEPersonas Overview • Platform administrator (siteadministrator)• Project administrator• Project memberProject Repository Setup • Initial access to HPE EzmeralContainer Platform• Setting up ML Ops environment and project repository• ML Ops clustersTraining 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 clustersNotebook Setup • Creating a notebook cluster• Notebook cluster configuration• More details on notebooks on ML Ops• Create notebook with training cluster• Review• Training first modelModel 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 modelInference • “Ready” deployment cluster• Doing inference• Walkthrough of scoring script• Local notebook to ML Ops training clusterLab 1: Initial Access to HPE Ezmeral Container Platform • Task 1: Initial log-on to HPE Ezmeral ContainerPlatform Management Console• Task 2: Lab system setup• Task 3: Initial log-on to controllerLab 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 repositoryLab 3: Create Training Clusters • Task 1: Create trainingclusterLab 4: Create Notebooks with Training Cluster • Task 1:Create notebook with training clusterLab 5: Training First Model • Task 1: Login to Jupyter hub •Task 2: Training the modelLab 6: Register and Deploy the Model • Task 1: Register themodel • Task 2: Deploy the modelLab 7: Inference • Task 1: Generate prediction requestsLab 8: Local Notebook to ML Ops Training Cluster • Task 1:Making required file configurations• Task 2: Accessing training cluster through JupyterNotebook• Task 3: Training the model through local notebookLab 9: Spark Deployment • Task 1: Setup Spark deploymentenvironment• 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

FOLLOW ON COURSES:
Not available. Please contact.