- Continuous Monitoring
- Software Development Life Cycle
- Data Quality
- Data Pipelines
- Data Validation
- MLOps (Machine Learning Operations)
- Feature Engineering
- Continuous Deployment
- Applied Machine Learning
- Machine Learning
- Application Deployment
Machine Learning in Production
Completed by Adam Niven Shumaker
January 3, 2022
11 hours (approximately)
Adam Niven Shumaker'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
