ML in Production: Deploy & Scale AI Systems
Master end-to-end ML production systems with hands-on training in deployment, monitoring, and optimization.
Master end-to-end ML production systems with hands-on training in deployment, monitoring, and optimization.
In this comprehensive course led by Andrew Ng, students learn to design and implement production-ready machine learning systems. The program covers the complete ML project lifecycle, from scoping and data preparation to deployment and monitoring. Students gain practical experience with deployment patterns, concept drift handling, and error analysis techniques. The course emphasizes real-world challenges in production ML, including data pipeline management, model optimization, and system maintenance. Through hands-on projects and practical examples, learners develop the skills needed to build, deploy, and maintain ML systems at scale. Special attention is given to establishing baselines, improving model performance, and addressing common production challenges.
4.8
(3,041 ratings)
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English
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What you'll learn
Design and implement end-to-end ML production systems
Establish model baselines and address concept drift effectively
Build robust data pipelines for production environments
Implement deployment patterns and monitoring strategies
Optimize model performance using error analysis techniques
Develop scalable ML solutions for real-world applications
Skills you'll gain
This course includes:
310 Minutes PreRecorded video
6 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 3 modules in this course
This comprehensive course focuses on the practical aspects of deploying machine learning systems in production environments. The curriculum covers three main areas: ML lifecycle and deployment patterns, modeling challenges and strategies, and data definition and baseline establishment. Students learn to handle real-world challenges in production ML systems, including data pipeline management, model monitoring, and system optimization. The course emphasizes hands-on experience with practical tools and techniques used in professional ML engineering.
Overview of the ML Lifecycle and Deployment
Module 1 · 3 Hours to complete
Modeling Challenges and Strategies
Module 2 · 3 Hours to complete
Data Definition and Baseline
Module 3 · 4 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Pioneer in AI and Online Education
Andrew Ng is the Founder of DeepLearning.AI, a General Partner at AI Fund, and the Chairman and Co-Founder of Coursera, where he also serves as an Adjunct Professor at Stanford University. Renowned for his groundbreaking contributions to machine learning and online education, Dr. Ng has transformed countless lives through his work in AI, having authored or co-authored over 100 research papers in machine learning, robotics, and related fields. His notable past roles include serving as chief scientist at Baidu and leading the founding team of Google Brain. Currently, Dr. Ng focuses on his entrepreneurial ventures, seeking innovative ways to promote responsible AI practices across the global economy.
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4.8 course rating
3,041 ratings
Frequently asked questions
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