- Debugging
- Machine Learning
- Data Pipelines
- Data Preprocessing
- Applied Machine Learning
- Model Deployment
- MLOps (Machine Learning Operations)
- Model Evaluation
- Data Quality
- Data Validation
- Feature Engineering
- Cloud Deployment
Machine Learning in Production
Completed by Amine Lagzouli
April 1, 2022
11 hours (approximately)
Amine Lagzouli'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
