- Data Validation
- Applied Machine Learning
- Data Pipelines
- MLOps (Machine Learning Operations)
- Data Quality
- Continuous Monitoring
- Feature Engineering
- Machine Learning
- Software Development Life Cycle
- Continuous Deployment
- Application Deployment
Machine Learning in Production
Completed by Saha Tamoghna
April 14, 2022
11 hours (approximately)
Saha Tamoghna'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
