Automatic email detection, text and image processing, Logic Apps, Functions, Notebooks & more!
On the last few days of work before Christmas our internal architect team hosted a “Save Santa” hackathon where we were given some pain points that Santa and his elves may have to deal with in their IT system.
As these pains may be transferable beyond the North Pole, hereby my team’s complete Azure solution to automate and add AI capabilities to an email-triggered insight gathering pipeline.
The pains we are targeting:
- Overwhelming number of emails coming in that have to be answered / sorted / processed / dealt with. …
Reproducibility for data science environments at scale
A. Introduction
Azure Machine Learning (AML) workspaces are a great platform in which data scientists and data engineers can collaborate and work on different projects. It brings together notebook coding environments, compute targets to power your code, datasets & datastores to keep references of your data sources, and a way to track your experiments.
While most tasks around this workspace can be achieved through the User Interface or with the Cloud Shell / command line, once you scale out to a large number or workspaces or data sources it can become overwhelming to manage all your resources manually.
Iulia Feroli
Cloud Solution Architect at Microsoft NL focusing on Data & AI. Data Scientist and Story Teller. All opinions are my own
