- Feature Engineering
- Application Deployment
- Continuous Monitoring
- Data Quality
- Continuous Deployment
- Software Development Life Cycle
- Applied Machine Learning
- Machine Learning
- MLOps (Machine Learning Operations)
- Data Validation
- Data Pipelines
Machine Learning in Production
Completed by Soojin Kim
March 12, 2025
11 hours (approximately)
Soojin Kim's account is verified. Coursera certifies their successful completion of Machine Learning in Production
What you will 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 will gain
