- Model Evaluation
- Data Pipelines
- Data Quality
- Debugging
- Model Deployment
- Machine Learning
- Continuous Deployment
- Data Validation
- Cloud Deployment
- Applied Machine Learning
- Feature Engineering
- MLOps (Machine Learning Operations)
Machine Learning in Production
Completed by Daria Sergeevna Tkachova
December 29, 2022
11 hours (approximately)
Daria Sergeevna Tkachova'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
