- Application Deployment
- Continuous Monitoring
- Data Quality
- Continuous Deployment
- Software Development Life Cycle
- MLOps (Machine Learning Operations)
- Data-Driven Decision-Making
- Applied Machine Learning
- Data Pipelines
- Machine Learning
- Data Validation
- Feature Engineering
Machine Learning in Production
Completed by Hema Raikhola
March 26, 2024
11 hours (approximately)
Hema Raikhola'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
