- Cloud Deployment
- Machine Learning
- Data Pipelines
- Applied Machine Learning
- Model Evaluation
- Model Deployment
- MLOps (Machine Learning Operations)
- Data Quality
- Data Validation
- Data Preprocessing
- Debugging
- Feature Engineering
Machine Learning in Production
Completed by Lana Krumm
May 7, 2022
11 hours (approximately)
Lana Krumm'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
