- Data Validation
- Data Quality
- Continuous Monitoring
- Feature Engineering
- Software Development Life Cycle
- Continuous Deployment
- Data-Driven Decision-Making
- Applied Machine Learning
- MLOps (Machine Learning Operations)
- Application Deployment
- Machine Learning
- Data Pipelines
Machine Learning in Production
Completed by Manula Yasas Basnayake
October 7, 2021
11 hours (approximately)
Manula Yasas Basnayake'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
