- Cloud Deployment
- Data Preprocessing
- Continuous Deployment
- MLOps (Machine Learning Operations)
- Debugging
- Machine Learning
- Data Quality
- Data Pipelines
- Data Validation
- Model Evaluation
- Feature Engineering
- Applied Machine Learning
Machine Learning in Production
Completed by Ignacio Rafael Ferreira Urrutia
August 8, 2021
11 hours (approximately)
Ignacio Rafael Ferreira Urrutia'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
