- Continuous Monitoring
- Feature Engineering
- Continuous Deployment
- Data Pipelines
- Software Development Life Cycle
- Applied Machine Learning
- Machine Learning
- Data Validation
- MLOps (Machine Learning Operations)
- Application Deployment
- Data Quality
- Data-Driven Decision-Making
Machine Learning in Production
Completed by Gonzalo Estebaranz Rojo
September 28, 2021
11 hours (approximately)
Gonzalo Estebaranz Rojo'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
