Using HPE AI and Machine Learning Rev. 22.21

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COURSE MATERIALS
4 Topics
Certification Information
Lab Guide
Useful Resources
Contact Form
FEEDBACK FORM
1 Exercise
FEEDBACK FORM – HPE GreenLake Administrator Copy
MODULE 1 – Machine Learning and Deep Learning Fundamentals
4 Topics | 1 Exercise
MODULE 1 – Learning Objectives
MODULE 1 – Exercises
Self Assessment – Using HPE AI and Machine Learning Module 1
MODULE 1 – Summary
MODULE 1 – Resources
MODULE 2 – Why HPE Machine Learning Development Environment
4 Topics | 1 Exercise
MODULE 2 – Learning Objectives
MODULE 2 – Exercises
Self Assessment – Using HPE AI and Machine Learning Module 2
MODULE 2 – Summary
MODULE 2 – Resources
MODULE 3 – HPE Machine Learning Development Environment Architecture
4 Topics | 1 Exercise
MODULE 3 – Learning Objectives
MODULE 3 – Exercises
Self Assessment – Using HPE AI and Machine Learning Module 3
MODULE 3 -Summary
MODULE 3 – Resources
MODULE 4 – Using HPE Machine Learning Development Environment
4 Topics | 1 Exercise
MODULE 4 – Learning Objectives
MODULE 4 – Exercises
Self Assessment – Using HPE AI and Machine Learning Module 4
MODULE 4 – Summary
MODULE 4 – Resources
MODULE 5 – Qualifying Customers and Running a PoC
4 Topics | 1 Exercise
MODULE 5 – Learning Objectives
Module 5 – Exercises Copy
Self Assessment – Using HPE AI and Machine Learning Module 5
MODULE 5 – Summary
MODULE 5 – Resources
MODULE 6 – HPE Machine Learning Development System
4 Topics | 1 Exercise
MODULE 6 – Learning Objectives
MODULE 6 – Exercises
Self Assessment – Using HPE AI and Machine Learning Module 6
MODULE 6 – Summary
MODULE 6 – Resources
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MODULE 1 – Summary

Using HPE AI and Machine Learning Rev. 22.21 MODULE 1 – Machine Learning and Deep Learning Fundamentals MODULE 1 – Summary

 

Having completed this module, you should now be able to:
  • Have a conversation with customers about machine learning (ML) and deep learning (DL)
  • Understand the challenges customers face in training DL models

 

Companies implementing ML/DL face many challenges, particularly in:
  • Distributing training across multiple GPUs
  • Optimizing training with HPO/NAS
  • Promoting team collaboration

 

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