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,
Tensorflow, etc.)
• Experience in machine learning lifecycle
(e.g. model training/development and
model deployment)
• Bash/shell/python scripting
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
Join our public courses in our Switzerland facilities. Private class trainings will be organized at the location of your preference, according to your schedule.