- Data Validation
- Applied Machine Learning
- Data Pipelines
- Feature Engineering
- MLOps (Machine Learning Operations)
- Continuous Monitoring
- Continuous Deployment
- Application Deployment
- Software Development Life Cycle
- Machine Learning
- Data Quality
Machine Learning in Production
Completed by Afraz Ul Haque
September 28, 2023
11 hours (approximately)
Afraz Ul Haque'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
