- Machine Learning
- Model Evaluation
- Data Preprocessing
- Continuous Monitoring
- Debugging
- MLOps (Machine Learning Operations)
- Data Quality
- Feature Engineering
- Model Deployment
- Data Pipelines
- Cloud Deployment
- Data Validation
Machine Learning in Production
Completed by David NAISSE
May 17, 2021
11 hours (approximately)
David NAISSE'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
