Machine learning & Data science (DS/ML) tooling

An end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.

An API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages.

An interpreted high-level general-purpose programming language. Has a huge community in machine learning and computer vision.

A Python-based scientific computing package that uses the power of graphics processing units. Deep learning research platform built to provide maximum flexibility and speed. Tensor computations with strong GPU acceleration support and building deep neural networks on a tape-based autograd systems.

Language agnostic, interactive computing and it supports execution environments (aka kernels) in several dozen languages.

A Python-based ecosystem of open-source software for mathematics, science, and engineering. Provides more utility functions for optimization, stats and signal processing.

A Python library designed to support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. The core of NumPy is well-optimized C code.

Open source machine learning software that can be accessed through a graphical user interface, standard terminal applications, or a Java API. Contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization.

A real-time analytics database designed for fast slice-and-dice analytics ("OLAP" queries) on large data sets. Druid is most often used as a database for powering use cases where real-time ingest, fast query performance, and high uptime are important.

Machine learning library has a range of supervised and unsupervised learning algorithms. Provides various tools for model fitting, data preprocessing, model selection and evaluation, and many other utilities.

Comprehensive library for creating static, animated, and interactive visualizations in Python. Offers a viable open source alternative to MATLAB.

Visualization tookit designed for machine learning. Enables you to track various metrics such as accuracy and log loss on training or validation set.

A high-level, high-performance, dynamic general-purpose programming language. Designed to solve the “two language problem” by providing ease of use and speed. Targeting big data analytics, high-performance computing and running simulations for scientific and engineering research.

A language and environment for statistical computing and graphics. Popular language in the world of Data Science. It is heavily used in analyzing data that is both structured and unstructured.

A fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.

a Data-Driven Documents JavaScript library for producing dynamic, interactive data visualizations in web browsers using SVG, HTML and CSS.

An interface for Apache Spark in Python. provides a shell for analyzing your data interactively in a distributed environment.

An open source computer vision and machine learning software library written in C++. Contains more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms.

A fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala and Python, and an optimized engine that supports general execution graphs. Supports higher-level tools for SQL and structured data processing, machine learning, graph processing and data streaming.

An optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.

A Python data visualization library based on matplotlib. Provides a high-level interface for drawing attractive and informative statistical graphics.

A programming platform designed specifically for engineers and scientists to analyze and design systems and products that transform our world. Takes your ideas from research to production by deploying to enterprise applications and embedded devices, as well as integrating with Simulink® and Model-Based Design.

A Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy. Contains analysis tools for measuring privacy guarantees.

The TensorFlow Cloud repository provides APIs that ease the transition from local model building and debugging to distributed training and hyperparameter tuning on Google Cloud.

a scientific Programming Language, primarily intended for numerical computations with built-in 2D/3D plotting and visualization tools. Highly compatible with Matlab.

A library for performing TensorFlow model evaluation. Evaluating models on large amounts of data in a distributed manner.

An open-source AI layer for existing databases that allows you to effortlessly develop, train and deploy state-of-the-art machine learning models using SQL queries. Provide both graphical user-interface and plain SQL for building and deploying ML models.

A Python library to benchmark machine learning systems' vulnerability to adversarial examples.

An interactive Web service that creates graphical charts from user-supplied information. The user supplies data and a formatting specification expressed in JavaScript embedded in a Web page

A set of AI-centric extensions to JupyterLab Notebooks. Provides a Pipeline Visual Editor for building AI pipelines from JupyterLab notebooks.

An open-source Python framework for creating reproducible, maintainable and modular data science code. Follows the concepts of modularity, separation of concerns and versioning.

A framework with wide support for deep learning algorithms. Open-source, distributed, deep learning library for the JVM. Works with Spark and Hadoop on top of distributed CPUs or GPUs.

A Python library for topic modelling, document indexing and similarity retrieval with large corpora. Memory efficient by design. highly optimized and parallelized C routines are used at the core level.

A suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. Runs ML tasks on one GPU, multi GPUs and multi node GPU.

A library for advanced Natural Language Processing in Python and Cython. Comes with pretrained pipelines and currently supports tokenization and training for 60+ languages.

GPU-Accelerated Libraries for AI and HPC. A collection of libraries, tools, and technologies that deliver dramatically higher performance than alternatives across multiple application domains—from artificial intelligence to high performance computing.

Tool help protect private information. Securely draw insights from datasets that contain the private and sensitive personal information of its users.

An easy-to-use interface for expanding understanding of a black-box classification or regression ML model. Can perform inference on a large set of examples and immediately visualize the results in a variety of ways. Contains tooling for investigating model performance and fairness over subsets of a dataset.

A machine Learning Platform for Kubernetes. Designed for building, training, and monitoring large scale deep learning applications. Supports multi GPU environment and all the major deep learning frameworks.

An open-source deep learning software framework, used to train, and deploy deep neural networks. It is scalable, allowing for fast model training, and supports a flexible programming model and multiple programming languages.

Geographic information systems use GeoTIFF and other formats to organize and store gridded, or raster, datasets. Rasterio reads and writes these formats and provides a Python API based on N-D arrays.

Math Kernel Library for Deep Neural Networks. An open-source performance library written in C++ for improving deep learning applications on Intel CPUs and GPUs.