- Applied Machine Learning
- MLOps (Machine Learning Operations)
- Feature Engineering
- Data Validation
- Model Evaluation
- Continuous Monitoring
- Continuous Deployment
- Data Pipelines
- Debugging
- Data Preprocessing
- Machine Learning
- Data Quality
Machine Learning in Production
Completed by Lucas Alamis Braga
May 26, 2021
11 hours (approximately)
Lucas Alamis Braga'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
