- Feature Engineering
- Application Deployment
- Continuous Monitoring
- Data Quality
- Continuous Deployment
- Software Development Life Cycle
- Applied Machine Learning
- Machine Learning
- MLOps (Machine Learning Operations)
- Data Validation
- Data Pipelines
Machine Learning in Production
Completed by EDWARD Q BRIDGES
May 20, 2024
11 hours (approximately)
EDWARD Q BRIDGES'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
