- Data Pipelines
- Cloud Deployment
- Data Quality
- Data Preprocessing
- Debugging
- Model Deployment
- Model Evaluation
- Machine Learning
- Continuous Monitoring
- Continuous Deployment
- Data Validation
- Applied Machine Learning
Machine Learning in Production
Completed by Yevgen Iliashchienko
June 20, 2022
11 hours (approximately)
Yevgen Iliashchienko'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
