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.
• AI/ML application administration
experience (Spark, Jupyter Notebook,
• Experience in machine learning lifecycle
(e.g. model training/development and
• Bash/shell/python scripting
Machine Learning Ops Overview
• Creating an ML Ops tenant
• External authentication
• Project repository
• Source control
• Model registr
• Data sources
• App store
• Notebooks HPE
• 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
• 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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