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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Access provided by Vasyl Stefanyc Precarpathian National University
159,575 already enrolled
Gain insight into a topic and learn the fundamentals.
3,362 reviews
Intermediate level
Recommended experience
Flexible schedule
1 week at 10 hours a week
Learn at your own pace
98%
Most learners liked this course
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
- Machine Learning
- Model Optimization
- System Monitoring
- Data Maintenance
- Continuous Deployment
- MLOps (Machine Learning Operations)
- Data Preprocessing
- Data Quality
- Applied Machine Learning
- Data Collection
- Data Validation
- Model Evaluation
- Data Synthesis
- Model Training
- Data Integrity
- Unstructured Data
- Application Deployment
- Continuous Monitoring
Tools you'll learn
Details to know
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Assessments
6 assignments
Taught in English
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There are 3 modules in this course
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Showing 3 of 3362
EW
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.
RG
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
DC
Reviewed on May 20, 2021
Practical and well-structured advices throughout the lifecycle of ML. Examples from real world problems & experiences make the advices more tangible and helps to reflect on own problems.
