- Data Preprocessing
- Applied Machine Learning
- Model Evaluation
- Continuous Monitoring
- Feature Engineering
- Machine Learning
- Model Deployment
- Data Pipelines
- MLOps (Machine Learning Operations)
- Cloud Deployment
- Data Validation
- Continuous Deployment
Machine Learning in Production
Completed by Alexander Treiss
May 24, 2021
11 hours (approximately)
Alexander Treiss'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
