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HPE Ezmeral Unified Analytics: Data Science

H41CTS

Course ID

H41CTS

Duration

1 day

Format

ILT, VILT

Schedule, pricing & registration

Browse related courses

Overview

This course teaches you how to build, deploy, and run machine learning applications using HPE Ezmeral Unified Analytics. It also demonstrates the end-to-end machine learning lifecycle and discusses how to build scalable AI/ML applications with HPE Ezmeral Unified Analytics. The course is 30% lecture and 70% practical hands-on labs.


Course ID

H41CTS

Duration

1 day

Format

ILT, VILT

Schedule, pricing & registration

Browse related courses

  • Audience

    This course is ideal for those who build and train models in notebooks using HPE Ezmeral Unified Analytics, system administrators, integrators, data scientists, data engineers, and professionals who wants to implement an HPE Ezmeral Unified Analytics solution.


  • Prerequisites

    Before attending this course, you should have:

    • An understanding of Kubernetes or any container orchestration software
    • A basic understanding of big data open-source tools and frameworks
    • A basic understanding of HPE Ezmeral Data Fabric

  • Objectives

    After completing this course, you should be able to:

    • Describe the features and capabilities of HPE Ezmeral Unified Analytics Software (HPE EzUA)
    • Build and train models in notebooks or pipelines using Kubeflow on HPE EzUA
    • Design end-to-end workflow to generate training data using Feast in HPE EzUA
    • Discuss MLDE and GPU MIG partitioning
    • Demonstrate applying end-to-end machine learning lifecycle process with MLflow in EzUA
    • Design scalable AI/ML application using HPE EzUA

  • Course outline

Module 1: Introduction to HPE Ezmeral Unified Analytics Software (HPE EzUA)
  • Describe the features and capabilities of HPE EzUA
  • Understand data science components of HPE EzUA
  • Identify the navigation in the HPE EzUA software
  • Discuss steps to get started with HPE EzUA

Module 2: Build and Train Models in Notebooks or Pipelines Using Kubeflow
  • Use Kubeflow Notebooks on HPE EzUA
  • Define hyperparameter tuning
  • Configure Katib on HPE EzUA
  • Create and manage Kubeflow Notebook servers on HPE EzUA
  • Demonstrate hyperparameter tuning for news recommendation (Ray Tune) use case

Module 3: Workflows Using Feast
  • Describe Feast and its components
  • Design workflows using Feast in HPE EzUA
  • Demonstrate Feast ride sharing use case

Module 4: HPE ML Development Environment (MLDE) and GPU MIG Partitioning
  • Describe HPE ML development environment (MLDE)
  • Describe configuring MLDE for air-gapped environment
  • Describe GPU MIG partitioning on HPE EzUA

Module 5: Manage the ML Lifecycle
  • Demonstrate using notebooks with MLflow on HPE EzUA
  • Define KServe
  • Demonstrate MLflow bike sharing use case

Module 6: Scalable AI/ML Applications
  • Discuss Ray support on EzUA
  • State Ray components and terminologies
  • Demonstrate designing anomaly detection application on HPE EzUA
  • Demonstrate rent forecasting model
  • Demonstrate question answering model

5 reasons to choose HPE as your training partner

  1. Learn HPE and in-demand IT industry technologies from expert instructors.
  2. Build career-advancing power skills.
  3. Enjoy personalized learning journeys aligned to your company’s needs.
  4. Choose how you learn: in-person, virtually, or online—anytime, anywhere.
  5. Sharpen your skills with access to real environments in virtual labs.

Explore our simplified purchase options, including HPE Education Learning Credits.

  • Lab outline

Lab 1: Getting Started with HPE Ezmeral Unified Analytics
  • Task 1: Log in to HPE EzUA
  • Task 2: Add user

Lab 2: Working with Kubeflow Notebook
  • Task 1: Using Kubeflow Notebooks on HPE EzUA
  • Task 2: Creating and managing Kubeflow Notebook servers on HPE EzUA
    • Creating a notebook server
    • Managing a notebook server
  • Task 3: Designing workflows using Feast in HPE EzUA

Lab 3: Working with ML Algorithms and Data Science in Jupyter Notebook
  • Task 1: Intro to ML with multiple algorithms
  • Task 2: Explanatory data analysis in data science
  • Task 3: Unsupervised clustering
  • Task 4: Supervised regression
  • Task 5: Demonstrate ML using financial data on HPE EzUA
  • Task 6: Execute digit recognition using MNIST database on HPE EzUA
  • Task 7: Using various training operators with HPE EzUA

Lab 4: Working with Generative AI models
  • Task 1: Pull and run custom LLM model using Docker
  • Task 2: Invoke custom LLM model in Kubeflow Notebook
  • Task 3: Retrieval Augmented Generation (RAG) using Langchain

Learn more

hpe.com/ww/learnbigdata

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Hewlett Packard Enterprise shall not be liable for technical or editorial errors or omissions contained herein.

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a50013226enw, H41CTS, May 2025

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