Top 10 Python Machine Learning Libraries You Must Try in 2021

In 2021 AI advanced rapidly, and Python remains the dominant language for implementing these algorithms. Many companies and research groups released high‑performance open‑source libraries, including several excellent domestic projects. Below is a roundup of the ten best Python ML libraries to help solve various problems.

Awkward Array

Awkward Array handles nested, variable‑length data such as lists, records, mixed types, and missing values, offering a NumPy‑like interface. It claims speed and memory advantages over traditional NumPy and can operate on arrays of different lengths directly.

https://pypi.org/project/awkward/

Jupytext

Jupytext is a lightweight plugin that lets Jupyter Notebooks be stored as Markdown or script files, enabling version control, editing in any text editor, and notebook‑level Q&A checks. The project has earned over 5 k stars on GitHub.

https://github.com/mwouts/jupytext

Gradio

Gradio is a lightweight UI library, even lighter than Streamlit, allowing you to turn models into interactive web demos with drag‑and‑drop images, text, audio, etc. Setting share=True in launch() generates a shareable link, effectively a mini‑app. The repo has more than 4.5 k stars.

https://github.com/gradio-app/gradio

Hub

Hub excels at data management and preprocessing, handling any type or size of data stored in the cloud, enabling seamless access from any machine. It provides APIs for data versioning, conversion, and integration with tools like PyTorch. The project has over 4.1 k stars.

https://github.com/activeloopai/Hub

AugLy

AugLy, released by Facebook, is a data‑augmentation library supporting audio, text, image, and video, with more than 100 augmentation methods. It addresses the difficulty of labeling large datasets and offers human‑friendly features such as adding text or emojis to media and detecting hateful or copyrighted content. The repo has 4.1 k stars.

https://github.com/facebookresearch/AugLy

Evidently

Evidently generates interactive visual reports and JSON summaries for model monitoring directly from Pandas DataFrames or CSV files, usable within Jupyter Notebooks. It currently provides six report types, including data drift, target drift, and performance metrics for regression and classification models. The project has 1.8 k stars.

https://github.com/evidentlyai/evidently

YOLOX

YOLOX is an anchor‑free version of the popular YOLO object‑detection algorithm, offering a simpler design and better performance. It aims to bridge research and industry, and has attracted over 5.2 k stars on GitHub.

https://github.com/Megvii-BaseDetection/YOLOX

LightSeq

LightSeq, developed by ByteDance, is a high‑speed inference engine supporting models such as BERT, GPT, and Transformer. Benchmarks show it outperforms FasterTransformer, and it has received 1.9 k stars.

https://github.com/bytedance/lightseq

Greykite

Greykite, created by LinkedIn, is a comprehensive time‑series forecasting library featuring algorithms like Silverkite, Facebook Prophet, and Auto‑ARIMA. It offers an intuitive interface, fast predictions, and strong scalability, with 1.4 k stars.

https://github.com/linkedin/greykite

Jina & Finetuner

Jina is a neural‑search framework that lets anyone build scalable deep‑learning search applications in minutes. Finetuner works with Jina to tune neural networks for optimal search performance, making the stack accessible to beginners. The combined project has over 5 k stars.

https://github.com/jina-ai/finetuner