- Feature Engineering
- Continuous Monitoring
- Continuous Deployment
- Data Quality
- Data Preprocessing
- Model Deployment
- Debugging
- Data Pipelines
- Model Evaluation
- Machine Learning
- Data Validation
- Cloud Deployment
Machine Learning in Production
Completed by Alberto Diaz Durana
January 31, 2024
11 hours (approximately)
Alberto Diaz Durana'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
