In this Machine Learning in Production course, you will build intuition about designing a production ML system end-to-end: project scoping, data needs, modeling strategies, and deployment patterns and technologies. You will learn strategies for addressing common challenges in production like establishing a model baseline, addressing concept drift, and performing error analysis. You’ll follow a framework for developing, deploying, and continuously improving a productionized ML application.
Machine Learning in Production

Machine Learning in Production

Instructor: Andrew Ng
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158,159 already enrolled
3,356 reviews
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What you'll learn
Identify key components of the ML project lifecycle, pipeline & select the best deployment & monitoring patterns for different production scenarios.
Optimize model performance and metrics by prioritizing disproportionately important examples that represent key slices of a dataset.
Solve production challenges regarding structured, unstructured, small, and big data, how label consistency is essential, and how you can improve it.
Skills you'll gain
- Continuous Monitoring
- System Monitoring
- Data Collection
- Applied Machine Learning
- Continuous Deployment
- Application Deployment
- Model Optimization
- Data Quality
- Data Synthesis
- Machine Learning
- Unstructured Data
- Model Training
- Model Evaluation
- Data Integrity
- Data Maintenance
- Data Validation
- Data Preprocessing
- MLOps (Machine Learning Operations)
Tools you'll learn
Details to know

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There are 3 modules in this course
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Reviewed on Nov 15, 2024
I learned many new perspective on how I can build my machine learning product and some pitfalls that could happen. It gives me fundamental on how do I design my product better.
Reviewed on Jun 4, 2021
really a great course. It'll really change your way of thinking ML in production use and will help you better understand how can you leverage the power of ML in a way that I'll really create a value
Reviewed on Jan 7, 2023
I really enjoy participating in a great class like Andrew's class. It's full of useful and applicable points that I encounter during a real prj. Thanks for sharing this asset with us :))



