MLflow vs Streamlit: What are the differences?

Introduction

MLflow and Streamlit are both popular tools used in the field of machine learning and data science. While both tools are used for different purposes, they have some key differences that set them apart from each other.

1. Deployment and Visualization: MLflow is mainly used for managing the machine learning lifecycle, including experiment tracking, packaging code, model versioning, and deployment. It provides a unified platform for data scientists and engineers to organize and deploy their models. On the other hand, Streamlit is used for building interactive web applications with a focus on data visualization. It allows developers to easily create and share web-based tools for exploring and presenting data.

2. User Interface: MLflow provides a command-line interface (CLI) as well as a web-based user interface (UI) for interacting with its features. The CLI allows users to interact with MLflow from the command line, while the web-based UI provides a graphical interface for managing experiments, tracking metrics, and viewing model artifacts. Streamlit, on the other hand, provides a Python library that allows users to create web applications with a simple and intuitive interface. It provides a single function call for creating an application, and the code written in Streamlit is executed in the order it is written.

3. Experiment Tracking: MLflow provides built-in experiment tracking capabilities, allowing users to log parameters, metrics, and artifacts for each run of their machine learning code. This makes it easy to compare different runs and track the performance of models over time. Streamlit does not have built-in experiment tracking functionality, as its primary focus is on data visualization and web application development. However, it is possible to integrate Streamlit with other tools or libraries to track experiments and log metrics.

4. Collaboration and Sharing: MLflow allows users to easily share models and experiments with others by packaging them as Docker containers or Python libraries. This makes it simple to deploy models in production environments or share them with colleagues. Streamlit, on the other hand, allows users to create web applications that can be easily shared and accessed by others. These applications can be deployed on the web or shared as executable files, allowing for easy collaboration and sharing of data-driven applications.

5. Model Serving: MLflow provides functionality for deploying models as RESTful services, allowing models to be served and queried using HTTP requests. This makes it easy to integrate MLflow models with other systems or use them in production environments. Streamlit does not have built-in model serving capabilities, as its focus is on data visualization and web application development. However, Streamlit can be used to build web applications that consume MLflow models deployed elsewhere.

6. Interface Flexibility: MLflow provides a flexible interface for working with models and experiments. It supports multiple programming languages, including Python, R, and Java, and can be used with various machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn. Streamlit, on the other hand, is primarily focused on Python and provides a simplified interface for building web applications with data visualization capabilities.

In summary, MLflow and Streamlit are both powerful tools in the machine learning and data science ecosystem, but they serve different purposes. MLflow is a comprehensive platform for managing the machine learning lifecycle, while Streamlit is focused on creating interactive web applications for data visualization.