In this course, you will :
- Create an end-to-end ML production system, including project scoping, data requirements, modelling strategies, and deployment requirements.
- Create a model baseline, address concept drift, and demonstrate how to develop, deploy, and continuously improve a productionized ML application.
- Gather, clean, and validate datasets to create data pipelines. Using data lineage and provenance metadata tools, create a data lifecycle.
- Maintain and monitor a continuously operating production system using best practises and progressive delivery techniques.
- Introduction to Machine Learning in Production
- Machine Learning Data Lifecycle in Production
- Machine Learning Modeling Pipelines in Production
- Deploying Machine Learning Models in Production