Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
- machine-learning
- models
- ui
- ui-components
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Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
Data Apps & Dashboards for Python. No JavaScript Required.
🤗 The largest hub of ready-to-use datasets for AI models with fast, easy-to-use and efficient data manipulation tools
CVAT is a leading data annotation platform for image, video, audio, and 3D datasets. It offers open-source, cloud, and enterprise products, as well as labeling services, with AI-assisted labeling, quality assurance, team collaboration, analytics, and developer APIs.
A high-performance, zero-overhead, extensible Python compiler with built-in NumPy support
Parallel computing with task scheduling
A MNIST-like fashion product database. Benchmark :point_down:
AI Observability & Evaluation
Rich MySQL Terminal Client with AutoCompletion, Syntax Highlighting, and Dataframes
Low-code framework for building custom LLMs, neural networks, and other AI models
NumPy aware dynamic Python compiler using LLVM
Fast and Accurate ML in 3 Lines of Code
Build, Manage and Deploy AI/ML Systems
Efficiently computes derivatives of NumPy code.
Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
An open source extension that connects AI agents to computational notebooks in JupyterLab.
Visualize and compare datasets, target values and associations, with one line of code.
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner
Feature engineering and selection open-source Python library compatible with sklearn.
Lightweight and extensible compatibility layer between dataframe libraries!
Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models
No-code in the front, Python in the back. An open-source framework for creating data apps.
Cloud-native genomic dataframes and batch computing