- Applied Machine Learning
- Data Management
- Data Preprocessing
- Model Evaluation
- MLOps (Machine Learning Operations)
- Predictive Modeling
- Artificial Intelligence and Machine Learning (AI/ML)
- Feature Engineering
- Model Deployment
- Continuous Deployment
- Data Pipelines
- Machine Learning
Machine Learning in Production
Completed by Mohammad Emamul Andalib
December 10, 2023
11 hours (approximately)
Mohammad Emamul Andalib'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
