Machine Learning Engineering for Production : MLOps

Description

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.

Syllabus :

• Introduction to Machine Learning in Production
• Machine Learning Data Lifecycle in Production
• Machine Learning Modeling Pipelines in Production
• Deploying Machine Learning Models in Production