- Data Quality
- Software Development Life Cycle
- Application Deployment
- Applied Machine Learning
- Machine Learning
- Data Validation
- Data Pipelines
- MLOps (Machine Learning Operations)
- Continuous Monitoring
- Continuous Deployment
- Feature Engineering
Machine Learning in Production
Completed by Nishanth Kumar Sivakumar
August 24, 2023
11 hours (approximately)
Nishanth Kumar Sivakumar'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
