Pipelines Libraries

With Dagster, you declare—as Python functions—the data assets that you want to build. Dagster then helps you run your functions at the right time and keep your assets up-to-date. Here is an example o…

A simple YAML API to get started quickly, a powerful Python API for total flexibility. Automatically cache your pipeline’s previous results and only re-compute tasks that have changed since your last…

  Taipy is designed for data scientists and machine learning engineers to build full-stack apps.         To install Taipy stable release run:   Below is our filter function. This is a typical Python …

These three functionalities enable a variety of use cases for data scientists, machine learning engineers, and data engineers: From here, you can quickly log a dataset: And there you have it, you now…

Automated machine learning for production and analytics auto_ml is designed for production. Here's an example that includes serializing and loading the trained model, then getting predictions on sing…

Covalent is a Python library for AI/ML engineers, developers, and researchers. It provides a straightforward approach to running compute jobs, like LLMs, generative AI, and scientific research, on v…

or: Alternatively, you could clone and run setup.py file: Initialize a group of classifiers as base estimators Initialize, fit, predict, and evaluate with Stacking See a sample output of classifier_s…

pypyr is a free & open-source task-runner that lets you define and run sequential steps in a pipeline. Like a turbo-charged shell script, but less finicky. Less annoying than a makefile. pypyr ru…

Foremast is a Spinnaker pipeline and infrastructure configuration and templating tool. Just create a couple JSON configuration files and then manually creating Spinnaker pipelines becomes a thing of…

This book teaches you how to take machine learning models from your personal laptop to large distributed clusters. You’ll explore key concepts and patterns behind successful distributed machine learn…

As we all know the Machine Learning space has a lot of tools and libraries for creating pipelines to train, test & deploy models, and dealing with these many different APIs can be cumbersome. Our…

Beneath is a serverless real-time data platform. Our goal is to create one end-to-end platform for data workers that combines data storage, processing, and visualization with data quality management …