Streamlit is an open-source Python library that makes it easy to create and share beautiful, custom web apps for machine learning and data science. Build an app in a few lines of code with magically simple API. Adding a widget is the same as declaring a variable. No need to write a backend, define routes etc. Effortlessly share, manage, and collaborate on your apps directly from Streamlit. Apps are deployed directly from Github repo.

Streamlit - The fastest way to build data apps in Python

AutoGluon automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy machine learning and deep learning models on image, text, and tabular data.
 
<ul>
<li>Quickly prototype deep learning and classical ML solutions for your raw data with a few lines of code.</li>
<li>Automatically utilize state-of-the-art techniques (where appropriate) without expert knowledge.</li>
<li>Leverage automatic hyperparameter tuning, model selection/ensembling, architecture search, and data processing.</li>
<li>Easily improve/tune your bespoke models and data pipelines, or customize <cite>AutoGluon</cite> for your use-case.</li>
</ul>

AutoGluon automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy machine learning and deep learning models on image, text, and tabular data.

AutoGluon - AutoML for Text, Image, and Tabular Data

Ray is an open-source unified compute framework that makes it easy to scale AI and Python workloads — from reinforcement learning to deep learning to tuning, and model serving. Ray is a unified way to scale Python and AI applications from a single node to a cluster.
 
Ray AI Runtime (AIR) is an open-source toolkit for building ML applications. It provides libraries for distributed data processing, model training, tuning, reinforcement learning, model serving, and more.
 
Ray Core provides a simple and flexible API for building and running your distributed applications. You can often parallelize single machine code with little to zero code changes.
 
With a Ray cluster you can deploy your workloads on AWS, GCP, Azure or on premise. You can also use Ray cluster managers to run Ray on your existing Kubernetes, YARN, or Slurm clusters.

Ray is an open-source unified compute framework that makes it easy to scale AI and Python workloads — from reinforcement learning to deep learning to tuning, and model serving. Ray is a unified way to scale Python and AI applications from a single node to a cluster.


Ray - A unified framework for scaling AI and Python applications

Rubrix is a production-ready Python framework for exploring, annotating, and managing data in NLP projects.
Most annotation tools treat data collection as a one-off activity at the beginning of each project. In real-world projects, data collection is a key activity of the iterative process of ML model development. Once a model goes into production, you want to monitor and analyze its predictions, and collect more data to improve your model over time. Rubrix is designed to close this gap, enabling you to iterate as much as you need.

RUBRIX -  Python framework to explore, label, and monitor data for NLP

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Streamlit - The fastest way to build data apps in Python

AutoGluon - AutoML for Text, Image, and Tabular Data

Ray - A unified framework for scaling AI and Python applications

RUBRIX - Python framework to explore, label, and monitor data for NLP

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Streamlit - The fastest way to build data apps in Python

AutoGluon - AutoML for Text, Image, and Tabular Data

Ray - A unified framework for scaling AI and Python applications

RUBRIX - Python framework to explore, label, and monitor data for NLP

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