Machine Learning in Production
Completed by Mateo D. Williams
January 29, 2026
11 hours (approximately)
Mateo D. Williams'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
- Category: Model Evaluation
- Category: Data Validation
- Category: Model Deployment
- Category: Data Integrity
- Category: Data Quality
- Category: Model Optimization
- Category: System Monitoring
- Category: Machine Learning
- Category: MLOps (Machine Learning Operations)
- Category: Continuous Deployment
- Category: Data Maintenance
- Category: Data Preprocessing
