Deploying Flask Applications in Microsoft Azure Cloud with Confidence

Image by the Author made with DALL•E 2. Prompt: “cloud and azure blue heaven with a flask in front, colorful, oil painting”

When serving machine learning models within an organization, you might face potential questions, like

1. How to efficiently provide your model to others in your company?
2. How to design security features in order that your model is only available within your company (or specific departments)?
3. How to maintain and continuously adjust the application?

In the following article, I will walk you through the process of sustainably deploying a Flask web application via the Microsoft Azure Cloud and securing it with Azure Active Directory Authentication.

Although building high-quality machine learning models is a significant accomplishment, sharing them with colleagues can often present its own set of obstacles. While lightweight frameworks like Streamlit can be helpful in the early stages of sharing, they may have limitations in terms of customization, organizational app deployment, and security and privacy assurance, particularly if personal or organizational data is involved. A potential solution to these challenges is to embed the model in your company’s internal cloud environment, which can provide greater control and integration with other applications in your technology stack.