Machine Learning Frameworks Software and Tools

AWS Deep Learning AMIs are Amazon Machine Images pre-configured with deep learning frameworks and tools, enabling developers and researchers to quickly set up and run machine learning models on Amazon EC2 instances.

AWS DeepLens is a deep learning-enabled video camera designed to help developers build, train, and deploy machine learning models for real-time computer vision applications. It enables users to run deep learning models locally on the device for tasks such as object detection, image recognition, and activity recognition.

AWS Inferentia is a custom machine learning inference chip designed by AWS to accelerate deep learning workloads. It provides high performance and cost-effective inference for models built using frameworks like TensorFlow, PyTorch, and MXNet.

AWS SageMaker is a fully managed service that enables developers and data scientists to build, train, and deploy machine learning models quickly. It provides tools for every step of the machine learning workflow, including data labeling, model tuning, and deployment to production.

Acumos AI is an open-source platform and framework that simplifies the development, sharing, and deployment of AI models. It provides a marketplace for AI solutions, enabling collaboration and integration of models into applications.

Alibaba AI Workspace is a comprehensive platform that provides tools and services for developing, deploying, and managing AI applications. It integrates various machine learning frameworks, data processing capabilities, and collaborative features to streamline the creation of AI models and solutions.

Alibaba DSW (Data Science Workshop) is a cloud-based integrated development environment for data science and machine learning. It provides tools for data analysis, model training, and deployment, facilitating collaboration and scalability in machine learning projects.

Alibaba PAI is a machine learning platform that provides tools and services for building, deploying, and managing AI models. It supports data preprocessing, model training, and model deployment, enabling users to create scalable AI solutions efficiently.

Apache MXNet is an open-source deep learning framework designed to train and deploy neural networks. It provides a flexible and efficient platform for developing machine learning models, supporting both symbolic and imperative programming to maximize speed and usability.

Apple Core ML is a machine learning framework that enables developers to integrate machine learning models into iOS, macOS, watchOS, and tvOS applications. It allows for on-device processing, providing fast and efficient performance while maintaining user privacy.

CatBoost is a gradient boosting library that uses categorical features efficiently and provides high-performance machine learning models for classification, regression, and ranking tasks.

The Cerebras Wafer-Scale Engine (WSE) is a specialized semiconductor designed for artificial intelligence and machine learning workloads. It provides massive computational power and memory bandwidth to accelerate training and inference processes in AI models.

Dask-ML is a scalable machine learning library built on Dask, designed to parallelize and distribute machine learning tasks across multiple CPUs or clusters. It integrates with existing machine learning libraries like Scikit-Learn to handle large datasets more efficiently.

Databricks MLflow is an open-source platform for managing the end-to-end machine learning lifecycle, including experimentation, reproducibility, and deployment. It provides tools to track experiments, package code into reproducible runs, and manage models in a central repository.

DataRobot is an AI-driven platform designed to automate the end-to-end process of building, deploying, and managing machine learning models. It helps organizations leverage data to make better decisions by providing tools for data preparation, model training, evaluation, and deployment, enabling users to implement predictive analytics with minimal manual intervention.

DeepMind AlphaZero is an advanced artificial intelligence program developed by DeepMind that uses reinforcement learning to master complex games like chess, shogi, and Go without prior knowledge of the rules, relying solely on self-play to achieve superhuman performance.

DeepMind Deep Q-Network (DQN) is a reinforcement learning algorithm that combines Q-learning with deep neural networks to enable agents to learn optimal policies for decision-making tasks directly from high-dimensional sensory inputs, such as raw pixels in video games.

DeepMind Sonnet is a high-level library built on TensorFlow for constructing neural networks. It facilitates the creation and training of complex machine learning models by providing modular and reusable components, streamlining the development process for researchers and developers.

Determined AI is an open-source deep learning training platform designed to streamline the development and training of machine learning models. It provides tools for efficient resource management, hyperparameter tuning, and distributed training, enabling data scientists to accelerate their workflows and improve model performance.

Facebook Caffe is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and later adopted by Facebook. It is designed for speed and modularity, enabling efficient training and deployment of deep neural networks, particularly for computer vision tasks.

Facebook PyTorch is an open-source machine learning library that provides tools for deep learning and tensor computation. It facilitates the development and training of neural networks through a flexible, easy-to-use interface, supporting dynamic computational graphs and offering extensive GPU acceleration.

Falkon is a machine learning library designed for large-scale kernel methods. It focuses on efficient training and prediction with large datasets by leveraging techniques like the Nyström method and conjugate gradient solvers to approximate kernel functions, making it suitable for tasks requiring high computational efficiency and scalability.

Fast.ai is an open-source deep learning library that simplifies training and deploying machine learning models. It provides high-level components that allow users to quickly implement state-of-the-art models with minimal code, making deep learning more accessible.

FastAPI is a modern, fast web framework for building APIs with Python 3.6+ based on standard Python type hints. It allows for the creation of robust APIs quickly and efficiently, leveraging asynchronous programming and automatic interactive API documentation.

Google AI Platform is a managed service that allows developers and data scientists to build, deploy, and manage machine learning models. It provides tools for every step of the ML lifecycle, from data preparation to training and prediction.

Google AI Platform Notebooks is a managed service that provides Jupyter notebooks in the cloud, enabling users to develop, train, and deploy machine learning models using powerful computational resources and integrated tools.

Google AdaNet is a lightweight framework for automatically learning high-quality models with minimal human intervention. It simplifies the process of neural architecture search, allowing users to build and train machine learning models efficiently by iteratively adding subnetworks to improve performance.

Google AutoML is a suite of machine learning tools and services that enables developers to train high-quality custom models with minimal effort and expertise. It automates the process of model selection, training, and deployment, making it easier to implement machine learning solutions.

Google AutoML Video is a machine learning service that enables developers to train custom video models for tasks such as object detection, activity recognition, and video classification without requiring extensive expertise in machine learning. It automates the process of building and deploying high-quality models by providing an easy-to-use interface and leveraging Google's advanced machine learning infrastructure.

Google BERT for Text is a natural language processing model designed to improve the understanding of context in search queries by considering the full context of a word by looking at the words that come before and after it. It helps in providing more relevant search results.

Google BigQuery ML is a service that enables users to create and execute machine learning models directly within BigQuery using SQL queries. It simplifies the process of integrating machine learning into data analysis by leveraging the scalability and performance of BigQuery's data warehouse capabilities.

Google Cloud AutoML is a suite of machine learning products that enables developers with limited expertise to train high-quality models tailored to their needs. It automates the process of building, training, and deploying machine learning models by leveraging Google's advanced neural architecture search technology.

Google Cloud AutoML Vision is a machine learning service that enables developers to train custom image recognition models. It automates the process of building and optimizing these models, allowing users to upload images, label them, and create highly accurate image classification or object detection models without extensive machine learning expertise.

Google Cloud ML Engine is a managed service that enables developers and data scientists to build, train, and deploy machine learning models at scale. It supports popular frameworks like TensorFlow and offers tools for preprocessing data, training models using distributed computing resources, and deploying trained models for online predictions.

Google Colaboratory, or Colab, is a cloud-based Jupyter notebook environment that allows users to write and execute Python code in a web browser. It provides free access to computing resources, including GPUs and TPUs, making it ideal for machine learning and data analysis tasks.

Google Coral is a platform that provides hardware and software tools for building edge AI applications. It includes development boards, accelerators, and modules equipped with the Edge TPU, which enables fast machine learning inference on-device, reducing latency and improving privacy by keeping data local.

Google DeepLab is a state-of-the-art deep learning model for semantic image segmentation, which aims to label each pixel in an image with a corresponding class. It uses convolutional neural networks (CNNs) to achieve high accuracy in identifying and delineating objects within images.

Google Differential Privacy is a technology that helps protect individual data privacy by adding controlled noise to datasets, allowing for the extraction of useful insights without revealing personal information.

Google Edge TPU is a specialized hardware accelerator designed for running machine learning models on edge devices. It enables low-latency, high-efficiency inferencing for applications such as image recognition, object detection, and natural language processing, directly on devices like IoT gadgets and embedded systems.

Google Federated Learning is a machine learning technique that trains algorithms across multiple decentralized devices holding local data samples, without exchanging them. It enhances privacy by keeping data on the device while only sharing model updates, thus allowing for collaborative learning without compromising user data security.

Google Federated Learning ML Kit is a machine learning technology that enables model training across multiple devices without centralizing data. It allows data to remain on users' devices, enhancing privacy while collaboratively improving the model.

Google Flax is an open-source machine learning library designed for building neural networks in Python, leveraging JAX for high-performance numerical computing. It provides a flexible and extensible framework to create and train complex models with ease.

Google JAX is an open-source machine learning library developed by Google Research that facilitates high-performance numerical computing and automatic differentiation for machine learning research. It enables users to transform numerical functions into optimized, parallelized, and GPU/TPU-accelerated code.

Google Ludwig is an open-source toolbox that simplifies the training and testing of deep learning models. It allows users to build, train, and evaluate machine learning models without writing code by using a declarative configuration file.

Google ML Kit is a mobile SDK that provides machine learning functionalities for Android and iOS apps. It offers pre-built APIs for common tasks like text recognition, face detection, image labeling, and language translation, allowing developers to integrate machine learning capabilities without needing extensive expertise in the field.

Scikit-learn is an open-source machine learning library for Python, providing simple and efficient tools for data mining and data analysis. It features various classification, regression, and clustering algorithms, and is designed to interoperate with other Python libraries such as NumPy and SciPy.

Google Sycamore is a quantum processor developed by Google designed to perform quantum computations. It aims to demonstrate quantum supremacy by solving specific tasks faster than classical supercomputers.

Google TFX (TensorFlow Extended) is an end-to-end platform for deploying production machine learning pipelines. It facilitates data validation, preprocessing, model training, and serving, ensuring scalable and reliable ML workflows.

Google TPUs (Tensor Processing Units) are custom-developed application-specific integrated circuits (ASICs) designed to accelerate machine learning workloads, specifically for TensorFlow. They offer high performance and efficiency for training and inference tasks in deep learning models.

Google TensorFlow is an open-source machine learning framework used for building, training, and deploying machine learning models. It provides a comprehensive ecosystem of tools, libraries, and community resources that enable developers to create deep learning models and perform complex numerical computations efficiently.

Google Vertex AI is a managed machine learning platform that allows developers to build, deploy, and scale ML models using pre-trained and custom tooling. It integrates various Google Cloud services to streamline the end-to-end ML workflow, including data preparation, model training, evaluation, and deployment.

Google Vertex Pipelines is a managed service that enables the orchestration of machine learning workflows on Google Cloud. It helps automate, monitor, and manage end-to-end ML processes, from data preparation to model training and deployment.

Graphcore Poplar is a software framework designed to optimize the performance of machine learning models on Graphcore's Intelligence Processing Units (IPUs). It provides tools and libraries for efficient model development, deployment, and execution, enabling high-speed training and inference for AI applications.

H2O Driverless AI is an automated machine learning platform that simplifies the process of building and deploying predictive models by automating feature engineering, model selection, hyperparameter tuning, and model validation. It leverages advanced techniques to produce high-performing models with minimal human intervention.

H2O.ai is an open-source machine learning platform that provides tools for building and deploying predictive models. It enables users to perform data analysis, model training, and scoring using various algorithms and supports integration with popular programming languages like Python, R, and Java.

Hugging Face Datasets is a library that provides easy-to-use tools for accessing and managing large datasets for machine learning and natural language processing tasks. It offers efficient dataset loading, preprocessing, and transformation capabilities, supporting a wide range of data formats and enabling seamless integration with popular ML frameworks.

Hugging Face Transformers is a library that provides pre-trained models for natural language processing tasks, such as text classification, translation, and question answering. It simplifies the use of transformer-based models like BERT, GPT-3, and T5 by offering easy-to-use APIs for model training and deployment.

IBM AI OpenScale is a platform that provides insights into AI models, ensuring they are fair, explainable, and compliant. It monitors and manages AI across its lifecycle, detecting biases, explaining decisions, and providing transparency in AI operations.

IBM Watson Machine Learning is a cloud-based platform that provides tools and services for building, training, deploying, and managing machine learning models. It enables data scientists and developers to leverage advanced algorithms and integrate AI into applications to make data-driven decisions.

Intel nGraph is an open-source deep learning compiler designed to optimize the performance of machine learning models across a variety of hardware platforms. It provides a unified framework for model optimization and execution, enhancing efficiency and scalability for both training and inference workloads.

Keras is an open-source neural network library written in Python, designed to enable fast experimentation with deep learning models. It provides a user-friendly interface for building and training neural networks, supporting both convolutional and recurrent networks, as well as combinations of the two. Keras runs on top of TensorFlow, Theano, or CNTK backends.

KubeFlow Pipelines is a platform for building, deploying, and managing end-to-end machine learning workflows on Kubernetes. It enables the orchestration of complex ML tasks, versioning of pipelines, and easy integration with other tools in the ML ecosystem.

Kubeflow is an open-source machine learning platform designed to simplify the deployment, orchestration, and management of machine learning workflows on Kubernetes. It provides tools for developing, training, and deploying machine learning models at scale.

LightGBM is a gradient boosting framework that uses tree-based learning algorithms. It is designed to be distributed and efficient, offering fast training speed, low memory usage, and the ability to handle large-scale data. LightGBM is commonly used for classification, regression, and ranking tasks.

MLFlow is an open-source platform for managing the end-to-end machine learning lifecycle, including experimentation, reproducibility, and deployment. It provides tools for tracking experiments, packaging code into reproducible runs, and sharing and deploying models.

Metaflow is a human-centered framework for data science that simplifies the process of building and managing real-life data science projects. It provides tools to seamlessly transition from prototyping to production, handling data management, versioning, and scaling workflows efficiently.

Microsoft Bonsai is an AI platform that enables developers to build, train, and deploy intelligent control systems using machine teaching. It simplifies the creation of AI models for automation and optimization tasks by leveraging simulation data and expert knowledge.

Microsoft CNTK, or Cognitive Toolkit, is a deep learning framework developed by Microsoft that enables users to train and evaluate deep neural networks. It is designed for performance and scalability, supporting both CPU and GPU computation, and is used for tasks such as image recognition, speech processing, and text analysis.

Microsoft Infer.NET is a machine learning framework for .NET that enables model-based inference. It allows developers to build and run Bayesian models, facilitating tasks such as probabilistic programming, data analysis, and predictions based on uncertain data.

Microsoft ML.NET is an open-source, cross-platform machine learning framework designed for .NET developers. It enables the development, training, and deployment of custom machine learning models using .NET languages such as C# and F#.

NVIDIA Clara is a healthcare application framework designed to support the development and deployment of AI-powered medical imaging, genomics, and smart sensor applications. It provides tools and APIs for creating scalable, secure, and efficient healthcare solutions leveraging NVIDIA's GPU technology.

NVIDIA NGC is a catalog of GPU-optimized software for deep learning, machine learning, and high-performance computing. It provides pre-trained models, model scripts, Helm charts, and containers to accelerate AI development and deployment.

NVIDIA RAPIDS is an open-source suite of software libraries and APIs designed to accelerate data science and analytics pipelines using NVIDIA GPUs. It facilitates faster data preparation, machine learning, and deep learning by leveraging GPU acceleration, thus significantly reducing processing times compared to traditional CPU-based methods.

NVIDIA Triton Inference Server is a scalable and extensible open-source platform that simplifies the deployment of AI models at scale. It supports multiple frameworks, optimizes inference performance, and manages model versions, enabling efficient and streamlined AI inference in production environments.

ONNX (Open Neural Network Exchange) is an open-source format designed to facilitate interoperability between different machine learning frameworks. It enables models to be transferred seamlessly across various platforms, making it easier to deploy and optimize machine learning models.

OpenAI Baselines is a set of high-quality implementations of reinforcement learning algorithms. It provides tools and libraries for developing, training, and evaluating reinforcement learning models efficiently.

OpenAI Gym is an open-source toolkit for developing and comparing reinforcement learning algorithms. It provides a variety of environments to simulate complex tasks, enabling researchers and developers to test and benchmark their models.

OpenVINO is a toolkit developed by Intel that facilitates the deployment of high-performance deep learning inference. It optimizes models for Intel hardware, enabling faster and more efficient execution of AI workloads on CPUs, GPUs, VPUs, and FPGAs.

Petastorm is an open-source data access library designed to enable the use of Apache Parquet datasets in deep learning frameworks. It facilitates efficient data loading and processing, supporting large-scale machine learning and AI workflows.

Petuum is a machine learning platform that provides tools and infrastructure to deploy, manage, and scale AI and machine learning models across various industries. It simplifies the process of integrating AI solutions by offering a comprehensive suite of services for data processing, model training, and deployment.

Polyaxon is a platform for managing and optimizing machine learning and deep learning workloads. It provides tools for orchestrating, monitoring, and scaling experiments in distributed environments.

Ray Tune is a scalable hyperparameter tuning library that integrates with the Ray distributed computing framework. It automates the process of searching for optimal hyperparameters in machine learning models, supporting various search algorithms and distributed execution to efficiently explore the hyperparameter space.

SAS Viya is a cloud-enabled, in-memory analytics engine that provides data management, advanced analytics, and machine learning capabilities. It allows users to analyze large volumes of data quickly and efficiently, enabling better decision-making through advanced statistical modeling and predictive analytics.

Seldon Core is an open-source platform that facilitates the deployment, scaling, and management of machine learning models on Kubernetes. It allows data scientists and engineers to serve models in production environments with features like model versioning, logging, monitoring, and A/B testing.

Skafos is a machine learning platform designed to simplify the deployment and management of machine learning models in mobile applications. It allows developers to integrate, update, and monitor ML models directly within their apps, facilitating real-time updates and performance tracking.

Snap ML is a machine learning framework developed by IBM that accelerates the training and inference of large-scale machine learning models. It leverages GPU and CPU hardware to significantly reduce computation time, enabling faster data processing and more efficient model deployment.

TensorFlow Hub is a repository and library for reusable machine learning modules, allowing users to easily incorporate pre-trained models into their own projects to accelerate development and improve performance.

TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow. It provides tools for building probabilistic models, performing inference, and leveraging machine learning techniques to handle uncertainty in data.

TensorRT is a high-performance deep learning inference optimizer and runtime library developed by NVIDIA. It accelerates the inference of deep learning models by optimizing neural network computations, reducing latency, and increasing throughput on NVIDIA GPUs.

Theano is an open-source numerical computation library for Python, designed to optimize and evaluate mathematical expressions, particularly those involving multi-dimensional arrays. It facilitates deep learning and machine learning by providing efficient symbolic differentiation and GPU acceleration.

Turi Create is a machine learning framework developed by Apple that simplifies the development of custom machine learning models. It provides tools for data scientists and developers to create, train, and deploy models for tasks such as image classification, object detection, and recommendation systems.

XGBoost is an open-source machine learning library that implements optimized gradient boosting algorithms. It is designed to be highly efficient, flexible, and portable, providing parallel tree boosting to solve many data science problems quickly and accurately.