Machine Learning Platforms, Technology, and Tools

  • Online

The course discusses the computational cyberinfrastructure that is necessary for enabling machine learning with big data. It explores big data lakes and data warehouses, discussing these two alternative enterprise repositories, and their relative strengths and drawbacks. Computational systems that facilitate machine learning in the enterprise are reviewed, as well as the concepts and techniques necessary for deploying scalable machine learning into business processes.

What you will learn:

  • Discuss computational cyberinfrastructure integration for machine learning
  • Review cyberinfrastructure for big data: data lakes and data warehouses
  • Examine computing ecosystems for enabling machine learning over enterprise data
  • Deploy scalable machine learning powered business insights and automation

This Course is part of the following Course Program:

Machine Learning: Predictive Analysis for Business Decisions

Courses included in this program:

Who should attend: Computer engineers, business executives, industry executives,  industry leaders, business leaders,  technical managers, data scientists, and data engineers


Grant Scott Photo

Grant Scott

Grant Scott is an Assistant Professor in the Center for Geospatial Intelligence (CGI) and the Electrical Engineering and Computer Science Department at the University of Missouri.

Throughout his career, Dr. Grant Scott has conducted extensive research on scaling machine learning up for big data. His research focuses on Applied Machine Learning, Computer Vision, as well as Advanced Pattern Analysis, High-dimensional Data Analytics, Advanced Data Systems, and Multi-modal Analytics.

Dr. Scott is a Senior Member of the IEEE Computational Intelligence Society and the IEEE Geoscience Remote Sensing Society.

Publication Year: 2020

ISBN: 978-1-7281-7748-9

Machine Learning Platforms, Technology, and Tools
  • Course Provider: Educational Activities
  • Course Number: EDP594
  • Duration (Hours): 1
  • Credits: 0.1 CEU/ 1 PDH