16 popular open-source contributions by big tech 👇

I prepared a visual that depicts the most popular open-source contributions by big tech in ML.

The motivation was to lay out and understand some of the key contributions from big techs in shaping the ML ecosystem over the years through their open-source contributions.

Microsoft:
- ​DeepSpeed​: A library for deep learning optimization, designed to train large models efficiently using features like model parallelism and mixed precision.
- ​LightGBM​: A gradient boosting framework optimized for speed and performance, commonly used for ranking, classification, and regression tasks.
- ​ONNX​: An open format to represent machine learning models, enabling interoperability between different frameworks.


Google:
- ​TensorFlow​: A widely-used deep learning framework for building, training, and deploying machine learning models across various platforms.
- ​JAX​: A library for high-performance numerical computing and deep learning, emphasizing differentiable programming and GPU/TPU support.
- ​OpenXLA​: An open-source compilation ecosystem for accelerating machine learning models on various hardware platforms.

Meta:
- ​Fairseq​: A sequence-to-sequence learning toolkit for training and evaluating custom models for tasks like machine translation, text generation, and speech recognition.
- ​PyTorch​: A popular machine learning framework known for its flexibility and ease of use in research and production environments.
- ​LLaMA​: A family of foundational language models designed for efficient and scalable NLP tasks.
- ​Faiss​: A library for efficient similarity search and clustering of dense vectors, widely used in recommendation systems and search.

Yandex:
- ​CatBoost​: A gradient boosting library optimized for categorical features, offering state-of-the-art accuracy and efficiency.
- ​YaFSDP​: An optimized data parallelism library that is an enhanced version of FSDP (a framework in PyTorch) with additional optimizations, especially for LLMs.
- ​ClickHouse​: A columnar database management system optimized for analytical queries and real-time data processing.

NVIDIA:
- ​RAPIDS​: A collection of GPU-accelerated libraries for data science, enabling faster data processing and machine learning workflows.
- TensorRT​: A platform for high-performance deep learning inference, offering model optimization and deployment on NVIDIA GPUs.
- ​NeMo​: A framework for building and fine-tuning large language models, particularly in speech, NLP, and multimodal AI.