- Machine Learning
- Software Development Life Cycle
- Application Deployment
- Data Quality
- Data Validation
- Data Pipelines
- Applied Machine Learning
- Feature Engineering
- Continuous Monitoring
- MLOps (Machine Learning Operations)
- Continuous Deployment
Machine Learning in Production
Completed by Aimilia Papagiannaki
January 6, 2024
11 hours (approximately)
Aimilia Papagiannaki'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
