There are a lot of libraries available in python apart from standard ones like sklearn which can be used by AI practitioners to improve their models in terms of speed, accuracy and explainability. Some of them are mentioned below:

• The fastai library simplifies training fast and accurate neural nets using modern best practices. See the fastai website to get started. The library is based on research into deep learning best practices undertaken at fast.ai, and includes "out of the box" support for vision, text, tabular, and collab (collaborative filtering) models.
• The autoai-lib library for Python contains a set of functions that help you to interact with IBM Watson Machine Learning AutoAI experiments. Using the autoai-lib library, you can review and edit the data transformations that take place in the creation of the pipeline. Documentation can be found on IBM Watson Studio cloud documentation, Using autoai-lib for Python
• The AI Fairness 360 toolkit is an open-source library to help detect and remove bias in machine learning models. The AI Fairness 360 Python package includes a comprehensive set of metrics for datasets and models to test for biases, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
• The AI Explainability 360 toolkit is an open-source library that supports interpretability and explainability of datasets and machine learning models. The AI Explainability 360 Python package includes a comprehensive set of algorithms that cover different dimensions of explanations along with proxy explainability metrics.
• GluonTS is a Python toolkit for probabilistic time series modeling, built around Apache MXNet (incubating).
• Prophet Implements a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.

If have also come across some useful library for developing AI applications, kindly share so that others may benefit as well!

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Ayush Jain
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