PyCaret- the open source low-code machine learning library in Python has come up with the new version PyCaret 2.0. The latest release aims to reduce the hypothesis to insights cycle time in a ML experiment, and enables data scientists to perform end-to-end experiments quickly and efficiently. Some major updates in the new release of PyCaret include:

• Logging back-end: Integrates MLFlow backend to track experiments (metrics, model parameters, artifacts, visuals etc.)
• Modular Automation: PyCaret 2.0 is an end-to-end workflow automation tool. You can use it to build automated machine learning workflows or even a front-end ML software.
• Command Line Interface (CLI): Optimize to work in Non Notebook environment such as Spyder, PyCharm, VS Code.
• GPU enabled training: Now you can train xgboost and catboost model using GPU.
• Parallel Processing: Supports parallel processing for almost all algorithms.
• Utility: Many new util functions introduced to help developers leverage more out of PyCaret.

What Is PyCaret?

In comparison with the other open source machine learning libraries, PyCaret is an alternate low-code library that can be used to perform complex machine learning tasks with only few lines of code. PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks such as scikit-learn, XGBoost, Microsoft LightGBM, spaCy and many more.

The design and simplicity of PyCaret is inspired by the emerging role of citizen data scientists, a term first used by Gartner. Citizen Data Scientists are power users who can perform both simple and moderately sophisticated analytical tasks that would previously have required more expertise. Seasoned data scientists are often difficult to find and expensive to hire but citizen data scientists can be an effective way to mitigate this gap and address data related challenges in business setting.

Utility

PyCaret is simple, easy to use and deployment ready. All the steps performed in a ML experiment can be reproduced using a pipeline that is automatically developed and orchestrated in PyCaret as you progress through the experiment. A pipeline can be saved in a binary file format that is transferable across environments.

The ideal target audience of PyCaret is:

• Experienced Data Scientists who want to increase productivity.
• Citizen Data Scientists who prefer a low code machine learning solution.
• Students of Data Science.
• Data Science Professionals and Consultants involved in building Proof of Concept projects.

Now that PyCaret 2.0 is available, the easiest way to install pycaret is using pip. It supports Python 64 bit only.

To learn more about the new features, please see the release notes