Machine Learning in Production
Completed by Andrew Saunov
November 25, 2024
11 hours (approximately)
Andrew Saunov'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
- Category: Model Evaluation
- Category: Data Collection
- Category: Data Validation
- Category: Applied Machine Learning
- Category: Data Synthesis
- Category: Application Deployment
- Category: MLOps (Machine Learning Operations)
- Category: Continuous Deployment
- Category: Data Maintenance
- Category: Data Preprocessing
- Category: Model Deployment
- Category: System Monitoring
