- Applied Machine Learning
- Model Deployment
- Cloud Deployment
- MLOps (Machine Learning Operations)
- Model Evaluation
- Data Quality
- Continuous Deployment
- Machine Learning
- Data Pipelines
- Continuous Monitoring
- Data Preprocessing
- Debugging
Machine Learning in Production
Completed by Tam Tue Nguyen
October 28, 2022
11 hours (approximately)
Tam Tue Nguyen'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
