Learn to lead machine learning projects through the complete lifecycle, from opportunity identification to deployment and monitoring.
Learn to lead machine learning projects through the complete lifecycle, from opportunity identification to deployment and monitoring.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full AI Product Management Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.8
(180 ratings)
15,196 already enrolled
Instructors:
Jon Reifschneider
English
پښتو, বাংলা, اردو, 2 more
What you'll learn
Identify valuable ML project opportunities
Apply data science process to organize projects
Make key ML system design decisions
Lead projects from concept to production
Manage model lifecycle and maintenance
Skills you'll gain
This course includes:
4.2 Hours PreRecorded video
5 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 5 modules in this course
This comprehensive course teaches the practical aspects of managing machine learning projects from inception to deployment. Students learn to identify ML opportunities, apply the CRISP-DM data science process, make key technology decisions, and manage production systems. The curriculum covers data collection, model building, deployment strategies, and system monitoring, preparing participants to lead ML initiatives in real-world settings.
Identifying Opportunities for Machine Learning
Module 1 · 2 Hours to complete
Organizing ML Projects
Module 2 · 2 Hours to complete
Data Considerations
Module 3 · 2 Hours to complete
ML System Design & Technology Selection
Module 4 · 2 Hours to complete
Model Lifecycle Management
Module 5 · 6 Hours to complete
Fee Structure
Instructor
Jon Reifschneider
4.8 rating
55 Reviews
56,564 Students
3 Courses
Director of AI for Product Innovation at Duke University
Jon Reifschneider is the Director of the Master of Engineering in Artificial Intelligence for Product Innovation (AIPI) program at Duke University's Pratt School of Engineering, where he also teaches graduate courses in machine learning. With a robust background in data services and analytics, Jon previously held senior management roles for 15 years, most notably as Senior Vice President at DTN, where he led the Weather Analytics division. His team developed predictive analytics systems that have become integral to the operations of major transportation, aviation, and energy utility organizations across the United States and globally. Jon's academic credentials include a B.S. in Mechanical Engineering from the University of Virginia, a Master of Engineering Management from Duke University, an M.S. in Analytics from Georgia Tech, and a Global MBA from EBS in Germany. His international experience spans the U.S., Luxembourg, Germany, and India, enriching his perspective on global engineering challenges. As a leader in integrating AI with product innovation, Jon Reifschneider is committed to advancing education and research in this dynamic field.
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4.8 course rating
180 ratings
Frequently asked questions
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