Optimizing Machine Learning Performance
Master ML optimization strategies, from model deployment to production maintenance. Perfect for real-world applications.
Master ML optimization strategies, from model deployment to production maintenance. Perfect for real-world applications.
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 Machine Learning: Algorithms in the Real World 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.
4.4
(48 ratings)
6,892 already enrolled
Instructors:
English
پښتو, বাংলা, اردو, 3 more
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What you'll learn
Develop comprehensive ML deployment strategies
Implement responsible AI practices in production
Optimize ML system performance and maintenance
Manage model versioning and updates
Design effective monitoring and logging systems
Skills you'll gain
This course includes:
2.4 Hours PreRecorded video
26 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course covers the complete lifecycle of machine learning projects in production. Students learn to develop ML strategies, implement responsible AI practices, and maintain deployed models. Topics include risk mitigation, team building, system integration, performance monitoring, and maintenance procedures. The curriculum emphasizes practical considerations for real-world ML deployments.
Machine Learning Strategy
Module 1 · 3 Hours to complete
Responsible Machine Learning
Module 2 · 1 Hours to complete
Machine Learning in Production & Planning
Module 3 · 1 Hours to complete
Care and Feeding of your Machine Learning System
Module 4 · 5 Hours to complete
Fee Structure
Instructor
Senior Scientific Advisor at the Alberta Machine Intelligence Institute (Amii), working to nurture productive relationships between industry and academia
working to nurture productive relationships between industry and academia and mainly focused on reinforcement learning, received her Master’s in Computing Science under the supervision of Dr. Richard Sutton, one of the field’s pioneer
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4.4 course rating
48 ratings
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.