- Data Validation
- Data Preprocessing
- Cloud Deployment
- Continuous Deployment
- Continuous Monitoring
- Machine Learning
- Model Evaluation
- Applied Machine Learning
- MLOps (Machine Learning Operations)
- Data Pipelines
- Feature Engineering
- Debugging
Machine Learning in Production
Completed by Pietro Rinaldi
December 20, 2022
11 hours (approximately)
Pietro Rinaldi'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
