- Data Quality
- Continuous Monitoring
- Data Pipelines
- Model Deployment
- Data Preprocessing
- Feature Engineering
- Machine Learning
- Continuous Deployment
- Data Validation
- Model Evaluation
- MLOps (Machine Learning Operations)
- Applied Machine Learning
Machine Learning in Production
Completed by Taras Zagibalov
February 4, 2023
11 hours (approximately)
Taras Zagibalov'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
