- Data Validation
- Continuous Monitoring
- Continuous Deployment
- Model Deployment
- MLOps (Machine Learning Operations)
- Applied Machine Learning
- Data Pipelines
- Machine Learning
- Debugging
- Data Quality
- Cloud Deployment
- Model Evaluation
Machine Learning in Production
Completed by Tengxiang Su
January 4, 2023
11 hours (approximately)
Tengxiang Su'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
