In this course you’ll explore how to turn promising ML prototypes into robust, scalable, and maintainable systems that deliver real value. Through hands-on demos, practical tools, and real-world case studies from companies like Netflix, Uber, and Google, you’ll gain a comprehensive understanding of what it takes to run ML systems effectively in production using MLOps.
Operationalizing ML Models: MLOps for Scalable AI
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Operationalizing ML Models: MLOps for Scalable AI
Instructors: Starweaver Instructor Team
Included with 
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
3 hours to complete
3 weeks at 1 hour a week
Flexible schedule
Learn at your own pace
What you'll learn
Implement scalable MLOps workflows that ensure efficient and reliable machine learning operations.
Build CI/CD pipelines for seamless and automated model updates, streamlining the development lifecycle.
Monitor deployed ML models for performance and drift.
Optimize AI infrastructure to handle scalability challenges and support high-performance deployments.
Details to know
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Recently updated!
March 2025
Assessments
1 assignment
Taught in English
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There is 1 module in this course
In this course, you’ll explore how to turn promising ML prototypes into robust, scalable, and maintainable systems that deliver real value. Through hands-on demos, practical tools, and real-world case studies from companies like Netflix, Uber, and Google, you’ll gain a comprehensive understanding of what it takes to run ML systems effectively in production using MLOps.
What's included
11 videos7 readings1 assignment1 peer review2 discussion prompts
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