- Data Validation
- Cloud Deployment
- Feature Engineering
- Applied Machine Learning
- Data Quality
- Model Evaluation
- Machine Learning
- Data Pipelines
- Data Preprocessing
- Debugging
- MLOps (Machine Learning Operations)
- Continuous Deployment
Machine Learning in Production
Completed by Anastasiia Pavlenko
May 1, 2024
11 hours (approximately)
Anastasiia Pavlenko'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
