Machine Learning in Production
Completed by Athanasios Papastefanopoulos
March 30, 2024
11 hours (approximately)
Athanasios Papastefanopoulos'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: Data Integrity
- Category: Machine Learning
- Category: Model Deployment
- Category: Data Validation
- Category: Data Quality
- Category: MLOps (Machine Learning Operations)
- Category: Data Preprocessing
- Category: System Monitoring
- Category: Continuous Monitoring
- Category: Model Training
- Category: Data Synthesis
- Category: Model Optimization
