Abstract
Objectives:
To educate attendees on processes and tools to deploy local python or R modelling code into high-quality, robust, scalable production code.
To provide attendees with a practical overview of Microsoft Azure Machine Learning Studio
Target Audience:
Data Science practitioners who write python or R code to build predictive models, and are interested in industry best practice for converting experimental code into reliable ML services for their users.
Agenda and Schedule:
Welcome and Introduction
1 hour – Session 1: Intro to Azure Machine Learning Studio
15 min – Break
1 hour – Session 2: Model Training and Inference
15 min – Break
45 min – Session 3: Deployment and Monitoring
15 min – Conclusion and Next Steps
