ML/DL in the Enterprise: Machine Learning Modeling, Development, & Deployment

Machine Learning    |    Intermediate
  • 13 Videos | 1h 9m 46s
  • Includes Assessment
  • Earns a Badge
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This 13-video course explores various standards and frameworks that can be adopted to build, deploy, and implement machine learning (ML) models and workflows. Begin with a look at the critical challenges that may be encountered when implementing ML. Examine essential stages of ML processes that need to be adopted by enterprises. Then explore the development lifecycle exclusively used to build productive ML models, and the essential phases of ML workflows. Recall the critical processes involved in training ML models; observe the various on-premises and cloud-based platforms for ML; and view the approaches that can be adopted to model and process data for productive ML deployments. Next, see how to set up a ML environment by using H2O clusters; recall various data stores and data management frameworks used as a data layer for ML environments; and specify the processes involved in implementing ML pipelines and using visualizations to generate insights. Finally, set up and work with Git to facilitate team-driven development and coordination across the enterprise. The concluding exercise concerns ML training processes.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    list critical challenges that may be encountered when implementing machine learning
    recognize the essential stages of machine learning processes that need to be adopted by enterprises
    describe the development lifecycle exclusively used to build productive machine learning models
    specify the essential phases of machine learning workflows and the functional flow of each phase
    recall the critical processes that are involved in training machine learning models
    list the various on-premise and cloud platforms that can be used to develop and deploy machine learning projects
  • describe the approaches that can be adopted to model and process data for productive machine learning deployments
    set up a machine learning development and deployment environment using H2O clusters
    recall the various data stores and data management frameworks that can be used as a data layer for machine learning environments
    specify the processes involved in implementing machine learning pipelines and using visualizations to generate insights
    set up and work with Git to facilitate team-driven development and coordination across the enterprise
    specify processes involved in training machine learning models, recall the prominent cloud platforms used to build and deploy machine learning projects, and set up machine learning deployment environment on AWS

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 51s
    UP NEXT
  • Playable
    2. 
    Challenges of Machine Learning
    6m 20s
  • Locked
    3. 
    Machine Learning Process Stages
    9m 50s
  • Locked
    4. 
    Machine Learning Development Lifecycle
    5m 22s
  • Locked
    5. 
    Machine Learning Workflow
    6m 59s
  • Locked
    6. 
    Machine Learning Training Process
    4m 54s
  • Locked
    7. 
    Machine Learning Platforms
    5m 16s
  • Locked
    8. 
    Machine Learning Data Modelling and Processing
    3m 53s
  • Locked
    9. 
    H2O Machine Learning Environment
    3m 29s
  • Locked
    10. 
    Data Source Management
    4m 5s
  • Locked
    11. 
    Machine Learning Pipeline
    4m 2s
  • Locked
    12. 
    Git Code Movement
    5m 45s
  • Locked
    13. 
    Exercise: Machine Learning Training Processes
    2m 32s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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Digital badges are yours to keep, forever.

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