ML/DL in the Enterprise: Pipelines & Infrastructure

Machine Learning
  • 10 Videos | 57m 15s
  • Includes Assessment
  • Earns a Badge
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Learners will discover the infrastructure, frameworks, and tools that can be used to build data pipelines and visualization for machine learning (ML) in this 10-video course exploring end-to-end approaches for building and deploying ML applications. You will begin with a look at approaches to identifying the right infrastructure for data and ML, and building data pipelines for ML deployments. Examine the iterative process in building ML models with Machine Learning Studio; implement machine learning visualization, and classify frameworks and tools for ML. Next, observe how to build generalized low-rank models by using H2O and integrate them into a data science pipeline to make better predictions. Explore the role of model metadata in applying governance in ML, and also ML risk mitigation—recognizing how ML risk analysis and management approaches can be used to mitigate risks effectively. In the exercise you will recall learning build and deployment frameworks, use Python to implement visualization for ML, and build a simple ML model by using Microsoft Azure Machine Learning Studio.

WHAT YOU WILL LEARN

  • 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

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 46s
    UP NEXT
  • Playable
    2. 
    Infrastructure for Data and Process
    14m 31s
  • Locked
    3. 
    Machine Learning and Data Pipeline
    3m 46s
  • Locked
    4. 
    Machine Learning Models
    6m 38s
  • Locked
    5. 
    Machine Learning Visualization
    3m 58s
  • Locked
    6. 
    Machine Learning Frameworks and Tools
    7m 6s
  • Locked
    7. 
    Working with H2O
    4m 2s
  • Locked
    8. 
    Model Metadata and Governance
    4m 32s
  • Locked
    9. 
    Risk Mitigation
    5m 38s
  • Locked
    10. 
    Exercise: Build Data Pipelines and Visualization
    1m 20s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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