- Data Quality
- Continuous Monitoring
- Data Pipelines
- Model Deployment
- Data Preprocessing
- Feature Engineering
- Machine Learning
- Continuous Deployment
- Data Validation
- Model Evaluation
- MLOps (Machine Learning Operations)
- Applied Machine Learning
Machine Learning in Production
Completed by Myron Sampsakis-Bakopoulos
April 9, 2022
11 hours (approximately)
Myron Sampsakis-Bakopoulos'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
