Scanning all new published packages on PyPI I know that the quality is often quite bad. I try to filter out the worst ones and list here the ones which might be worth a look, being followed or inspire you in some way.

A pandas and simple-salesforce based utility package

Validation and data pipelines made easy!

Plauthor: Machine Learning Focused Interface for Dataframe-related Visualizations


Prophesy – Parametric Probabilistic Model Checking

Various metrics for evaluating text generation models.

Waymo Open Dataset libraries.

Multi-Agent System (MAS) Framework for managing resource;It also providers a discrete event simulator component to evaluate agents before deploying them. ARPS is an acronym that comes from the simplest definition of an
software agent is:
• Autonomous: ability to run without human interaction
• Reflexive: ability to act upon events
• Proactive: ability to make its own decisions
• Social: ability to interact with other agents to better achieve its
Auger ML predict python and command line interface

A Jupyter server extension to proxy requests with AWS SigV4 authentication

Speech recognition for Danish

A light set of enabling convenience functions based on Cloudframe’s proprietary data science enablers. At Cloudframe we employ teams of Data Scientists, Data Engineers, and Software Developers. Check us out at [http://…/ ‘Cloudframe website’) If you’re interested in joining our team as a Data Scientist see here: [Bid Prediction Repo](https://…/texas-bid-prediction ). There you’ll find a fun problem and more info about our evergreen positions for Data Scientists, Data Engineers, and Software Developers. This package contains some convenience functions meant help a Data Scientist get data into a format that is useful for training models. It is a light version of some of our proprietary enablers that we use to deliver data-informed products to our clients.

A lightweight data-augmentation library for machine learning. elaugment is a Python package for reproducable data-augmentations. In difference to most other libraries random parameters for transformations are drawn seperate from the transformations. This makes it very easy apply the same transformations to several images. An example where this behaviour is useful is semantic segmentation, when you need to modify the input and the mask in the same way.

Python implementation of the k-t BLAST algorithm.

Kubeflow Python SDK.