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ML/DL in the Enterprise: Machine Learning Infrastructure & Metamodel

ML/DL in the Enterprise: Machine Learning Infrastructure & Metamodel


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Discover the infrastructure, frameworks, and tools that can be used to build data pipelines and visualization for machine learning. End-to-end approaches for building and deploying machine learning applications are also explored.



Expected Duration (hours)
0.9

Lesson Objectives

ML/DL in the Enterprise: Machine Learning Infrastructure & Metamodel

  • discover the key concepts covered in this course
  • list approaches for identifying the right infrastructure for data and machine learning processes
  • build data pipelines that can be used for machine learning deployments
  • describe the iterative process involved in building machine learning models
  • implement visualization for machine learning using Python
  • classify machine learning frameworks and tools for building and deploying machine learning applications
  • build generalized low rank models using H2O and integrate them into a data science pipeline to make better predictions
  • describe the role of model metadata in applying governance policies on machine learning
  • recognize how machine learning risk analysis and management approaches can be used to mitigate risks effectively
  • recall machine learning build and deployment frameworks, use Python to implement visualization for machine learning, and build a simple machine learning model using Machine Learning Studio
  • Course Number:
    it_mldmledj_02_enus

    Expertise Level
    Intermediate