ML/DL in the Enterprise: Pipelines & Infrastructure

Machine Learning    |    Intermediate
  • 10 videos | 53m 15s
  • Includes Assessment
  • Earns a Badge
Rating 3.9 of 76 users Rating 3.9 of 76 users (76)
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

  • 1m 46s
  • 14m 31s
    After completing this video, you will be able to list approaches for identifying the right infrastructure for data and machine learning processes. FREE ACCESS
  • Locked
    3.  Machine Learning and Data Pipeline
    3m 46s
    Learn how to build data pipelines that can be used for machine learning deployments. FREE ACCESS
  • Locked
    4.  Machine Learning Models
    6m 38s
    After completing this video, you will be able to describe the iterative process involved in building machine learning models. FREE ACCESS
  • Locked
    5.  Machine Learning Visualization
    3m 58s
    Learn how to implement visualization for machine learning using Python. FREE ACCESS
  • Locked
    6.  Machine Learning Frameworks and Tools
    7m 6s
    Learn how to classify machine learning frameworks and tools for building and deploying machine learning applications. FREE ACCESS
  • Locked
    7.  Working with H2O
    4m 2s
    In this video, you will build generalized low rank models using H2O and integrate them into a data science pipeline to make better predictions. FREE ACCESS
  • Locked
    8.  Model Metadata and Governance
    4m 32s
    Upon completion of this video, you will be able to describe the role of model metadata in applying governance policies to machine learning. FREE ACCESS
  • Locked
    9.  Risk Mitigation
    5m 38s
    Upon completion of this video, you will be able to recognize how machine learning risk analysis and management approaches can effectively mitigate risks. FREE ACCESS
  • Locked
    10.  Exercise: Build Data Pipelines and Visualization
    1m 20s
    After completing this video, you will be able to 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. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.

Digital badges are yours to keep, forever.

YOU MIGHT ALSO LIKE

Book Delivering Machine Learning Projects: From Design to Deployment
Book Practical Full Stack Machine Learning
Course Low-code ML with KNIME: Getting Started with the KNIME Analytics Platform
Rating 4.6 of 9 users Rating 4.6 of 9 users (9)

PEOPLE WHO VIEWED THIS ALSO VIEWED THESE

Book Python Data Analytics: Data Analysis and Science Using Pandas, Matplotlib, and the Python Programming Language
Course Refactoring ML/DL Algorithms: Refactor Machine Learning Algorithms
Rating 4.5 of 40 users Rating 4.5 of 40 users (40)
Course Machine Learning & Deep Learning Tools in the Cloud
Rating 4.0 of 40 users Rating 4.0 of 40 users (40)