How a simple streamlit dashboard will help to put your machine learning model in production

The end-goal of many machine learning projects is to get a valuable model into the hands of users that will benefit from it. No matter how good your ML model is; a lack of monitoring and insights can prevent users from being willing to use your product.

A monitoring dashboard is easy to build & serves two functions: a. Monitoring of driving performance metrics b. Delivering insights into the model & it’s predictions.

These two functions combined can greatly help towards the primary goal of getting a valuable model into the hands of users. A monitoring dashboard thus serves an important purpose. Luckily, such a dashboard can be built effortlessly using tools like streamlit. We will show how a minimalistic monitoring dashboard that serves both functions can be built using streamlit in only a few hours of work.

This talk will cover the following topics, linked to each other through real-world examples:

• My struggles of getting a model in use by willing users.
• Why you need to monitor performance metrics.
• Why performance metrics might not be enough & you might need more insights.
• How easy it is to do all this in streamlit.