We've all seen poor tooling slow down data science and machine learning projects. In fact, most projects develop their own unique ecosystem of bug-ridden and unmaintainable internal tools to analyze data, often through a patchwork of Jupyter Notebooks and Flask apps.

Along with rapid velocity of data ingestion, the need for speedier decision making will simultaneously rise. Today’s process of MLOps will become redundant as data scientists will start searching for faster ways to create production grade deployments with insightful user interfaces that communicate the power of the algorithms without getting stuck in long MLOps journeys.

In this workshop, we would be discussing about a new python library called “Streamlit” which helps data scientists rapidly create production-grade visualizations with backend integration and quickly share their results with stakeholders to generate powerful insights. Here we would utilize Streamlit in creating a web-based tool which runs on top of an optimization code, reducing our algorithm development to front end deployment lead time from “2-3 weeks” to “2-3 days”.

At the end of the workshop you will have (1) a beautiful demo , and (2) a new weapon to tackle tooling problems in your own projects.