How to Dockerize Machine Learning Applications Built with Streamlit and Python

This post was originally posted here by the author. This was written in collaboration with Soniya Mehta, a Data Scientist working at EXL Health.

Customer churn is a million-dollar problem for businesses today. The SaaS market is becoming increasingly saturated, and customers can choose from plenty of providers. Retention and nurturing are challenging. Online businesses view customers as churn when they stop purchasing goods and services. Customer churn can depend on industry-specific factors, yet some common drivers include lack of product usage, contract tenure, and cheaper prices elsewhere.

Limiting churn strengthens your revenue streams. Businesses and marketers must predict and prevent customer churn to remain sustainable. The best way to do so is by knowing your customers. And spotting behavioral patterns in historical data can help immensely with this. So, how do we uncover them?

Applying machine learning (ML) to customer data helps companies develop focused customer-retention programs. For example, a marketing department could use an ML churn model to identify high-risk customers and send promotional content to entice them.

To enable these models to make predictions with new data, knowing how to package a model as a user-facing, interactive application is essential. In this blog, we’ll take an ML model from a Jupyter Notebook environment to a containerized application. We’ll use Streamlit as our application framework to build UI components and package our model. Next, we’ll use Docker to publish our model as an endpoint.


Why choose Streamlit?

Streamlit is an open source, Python-based framework for building UIs and powerful ML apps from a trained model. It’s popular among machine learning engineers and data scientists as it enables quick web-app development — requiring minimal Python code and a simple API. This API lets users create widgets using pure Python without worrying about backend code, routes, or requests. It provides several components that let you build charts, tables, and different figures to meet your application’s needs. Streamlit also utilizes models that you’ve saved or pickled into the app to make predictions.

Conversely, alternative frameworks like FastAPI, Flask, and Shiny require a strong grasp of HTML/CSS to build interactive, frontend apps. Streamlit is the fastest way to build and share data apps. The Streamlit API is minimal and extremely easy to understand. Minimal changes to your underlying Python script are needed to create an interactive dashboard.

Getting Started

git clone

Key Components

  • An IDE or text editor
  • Python 3.6+
  • PIP (or Anaconda)
  • Not required but recommended: An environment-management tool such as pipenv, venv, virtualenv, or conda
  • Docker Desktop

Before starting, install Python 3.6+. Afterwards, follow these steps to install all libraries required to run the model on your system.

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