- Continuous Monitoring
- Data Validation
- Continuous Deployment
- MLOps (Machine Learning Operations)
- Machine Learning
- Data Quality
- Applied Machine Learning
- Feature Engineering
- Data Pipelines
- Software Development Life Cycle
- Application Deployment
Machine Learning in Production
Completed by Alexandra Keamy
July 11, 2024
11 hours (approximately)
Alexandra Keamy'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
