- Continuous Monitoring
- Continuous Deployment
- Model Evaluation
- Cloud Deployment
- Feature Engineering
- Data Preprocessing
- Data Pipelines
- MLOps (Machine Learning Operations)
- Model Deployment
- Applied Machine Learning
- Data Validation
- Debugging
Machine Learning in Production
Completed by Ekaterina Kuzmina
January 10, 2024
11 hours (approximately)
Ekaterina Kuzmina'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
