Evidently AI: Empowering Transparency in Machine Learning Model Analytics
Change and development are at the heart of technology. More than ever, businesses need tools that provide a clear, thorough view into the performance of their machine learning models. One tool that aims to fill this gap is Evidently AI, a public GitHub project that is reshaping the way businesses approach their data science efforts.
A brief introduction to the project:
Evidently AI encapsulates a myriad of functions intended to evaluate and visualize machine learning model performance and various data drifts. It's a light-weight package that yields transparent insights across the machine learning pipeline from data analysis to model analytics.
Project Overview:
Evidently AI's ultimate objective is to bring transparency to the forefront of the data science workflow. It addresses the need for clarity and interpretability in machine learning models, providing an in-depth examination of the model’s behavior. It is particularly useful for data scientists, machine learning engineers, and analysts who are keen to understand and track the performance of their predictive models.
Project Features:
Evidently AI's major features lie in its analytical capacity. This includes numerical and categorical target drift analysis, numerical and categorical feature drift analysis, and regression model performance analysis. For example, the tool can be used to monitor production models, compare model versions, analyze data subsets, and more. The insights exhibited aid in pinpointing issues in data science projects, allowing users to make informed decisions and improvements.
Technology Stack:
Evidently AI was built using Python, one of the leading programming languages in data analysis and machine learning. The depictions of analyses are produced with Plotly, a Python graphing library that creates interactive, publication-quality charts. This choice of technology stack allows for robust, fast, and efficient computation while offering flexibility and compatibility with a wide range of machine learning use cases.
Project Structure and Architecture:
On a high level, the Evidently AI project can be divided into three main components: the Dashboard class, the Profile class, and the Model Performance class. The Dashboard class calculates and visualizes data and target drifts, whereas the Profile class prepares data analytics. The Model Performance class is responsible for assessing and visually representing the behavior of the machine learning models. Combined, these layers offer a comprehensive view of machine learning models' performance.
Contribution Guidelines:
