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Instructors: Starweaver Instructor Team
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Recommended experience
Recommended experience
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.
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March 2025
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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.
11 videos7 readings1 assignment1 peer review2 discussion prompts
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