Machine Learning Frameworks Platforms

Lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention using adaptive neural networks.

Machine learning workflow orchestration platform providing end-to-end ML pipeline management with visual workflow design and scheduling.

Fast image augmentation library providing efficient implementations of diverse image transformations for computer vision and deep learning applications.

Collection of robust training recipes to align language models using supervised fine-tuning, reward modeling, and RLHF.

Natural language processing platform by Allen Institute for AI built on PyTorch for developing state-of-the-art deep learning models on NLP tasks.

Declarative statistical visualization library for Python based on Vega-Lite providing concise visualization grammar for exploratory data analysis.

Self-service data analytics platform with machine learning capabilities enabling business analysts to perform advanced analytics without coding.

Data discovery and metadata engine by Lyft providing search interface for data to improve productivity of data scientists and analysts.

C++ library with Python bindings for approximate nearest neighbors by Spotify, optimized for memory usage and fast queries.

Open-source metadata management and data governance framework enabling enterprises to meet compliance requirements with lineage tracking and classification.

Unified model for defining and executing data processing workflows including machine learning pipelines for both batch and streaming data.

Machine learning library for Apache Flink providing distributed ML algorithms and infrastructures for building scalable ML pipelines.

Scalable machine learning algorithms focused on linear algebra and distributed computing, primarily supporting Apache Spark for big data applications.

Flexible and efficient deep learning framework supporting multiple programming languages with symbolic and imperative programming paradigms.

Data flow automation platform with machine learning capabilities for building ML pipelines and real-time data processing workflows.

Distributed machine learning library that runs on Apache Spark, providing scalable algorithms for classification, regression, clustering, and collaborative filtering.

Open-source machine learning compiler framework for CPUs, GPUs, and ML accelerators enabling efficient model deployment across hardware.

Open-source container-native workflow engine for orchestrating parallel jobs on Kubernetes, commonly used for ML pipeline orchestration.

Open-source observability platform for evaluating, troubleshooting, and monitoring LLM, computer vision, and tabular models.

Automated machine learning toolkit built on scikit-learn that automatically finds the best machine learning pipeline for your dataset.

Microsoft's multi-agent conversational framework enabling multiple AI agents to collaborate and solve complex tasks through conversation.

AutoML toolkit by Amazon that enables easy-to-use and easy-to-extend automatic machine learning with a focus on deep learning and real-world applications.

Automated machine learning service in Amazon SageMaker that automatically builds, trains, and tunes machine learning models with full visibility.

Tool designed to streamline the fine-tuning of various AI models offering support for multiple configurations and architectures.

Automated machine learning service in Azure Machine Learning enabling democratized AI model development with code and no-code approaches.

Framework for serving, managing, and deploying machine learning models as production-ready API services with support for multiple ML frameworks.

Interactive visualization library for modern web browsers providing elegant construction of versatile graphics for large and streaming datasets.

JavaScript neural network library providing simple and fast neural networks in the browser and Node.js with GPU acceleration support.

Numerical processing library for Scala providing linear algebra, numerical routines, and integration with machine learning frameworks.

Python stream processing framework for building real-time data applications with stateful operations and exactly-once processing.

Enterprise AI application development platform providing tools for building, deploying, and operating AI applications at scale.

Deep learning framework made with expression, speed, and modularity in mind, particularly focused on convolutional neural networks for computer vision.

Classification and Regression Training package for R providing unified interface to hundreds of machine learning algorithms with data splitting and preprocessing.

Gradient boosting library by Yandex that excels with categorical features, providing symmetric trees and ordered boosting for enhanced performance.

Open-source Python framework for building conversational AI applications with interactive interfaces for LLM-powered chatbots.

Open-source embedding database designed for AI applications providing efficient storage and retrieval of vector embeddings.

Auto-magical suite of tools to streamline AI workflow including experiment management, MLOps/LLMOps, and data management capabilities.

End-to-end model evaluation platform for AI developers with experiment tracking, production monitoring, and LLM evaluation capabilities.

Apple's machine learning framework for integrating machine learning models into iOS, macOS, watchOS, and tvOS applications with on-device performance.

Comparative framework for multimodal recommender systems focusing on models that leverage auxiliary data like item/user features.

Open-source platform for deploying machine learning models as production web services, providing autoscaling and multi-framework support.

Python library for online machine learning providing incremental learning algorithms for streaming data and adaptive model training.

Framework for orchestrating role-playing autonomous AI agents enabling collaborative task completion through specialized agent roles.

GPU DataFrame library providing pandas-like API for GPU-accelerated data processing and analytics using NVIDIA RAPIDS ecosystem.

GPU-accelerated computing library for Python that provides NumPy-compatible array operations on NVIDIA CUDA and AMD ROCm platforms.

Data orchestrator for machine learning, analytics, and ETL providing asset-based orchestration with testing and observability features.

Flexible library for parallel computing in Python that scales NumPy, pandas, and scikit-learn workflows to larger datasets and distributed computing.

Scalable machine learning library providing parallel and distributed computing for scikit-learn compatible estimators using Dask.

Automated machine learning capabilities within Databricks platform providing glass-box approach to model development and deployment.

Centralized repository for storing, sharing, and serving machine learning features across the ML lifecycle in Databricks platform.

Unified metadata platform by Acryl Data for discovering, understanding, and managing data across your organization with real-time metadata ingestion.

Data science platform providing collaborative environment for building, deploying, and monitoring machine learning models with visual and code interfaces.

Enterprise AI platform providing automated machine learning, model deployment, and monitoring with enterprise-grade governance and explainability.

Graphics pipeline system for creating meaningful visualizations of large datasets using aggregation and rendering techniques.

Python library for comprehensively validating machine learning models and data with built-in checks for data integrity and model evaluation.

Easy-to-use package for CTR prediction with deep learning models supporting various features and recommendation systems.

Deep learning library for Java and Scala on JVM with distributed training capabilities and integration with Hadoop and Spark.

Facebook AI Research's next-generation platform for object detection and segmentation with state-of-the-art models and modular design.

Enterprise MLOps platform providing collaborative data science workspace with experiment tracking, model deployment, and governance features.

Framework for algorithmically optimizing LM prompts and weights using programming rather than prompting for complex language model tasks.

Version control system for machine learning projects, enabling data versioning, experiment tracking, and reproducible ML pipelines with Git integration.

Planetary-scale platform for Earth science data and analysis providing satellite imagery and geospatial datasets for environmental monitoring.

Probabilistic programming language designed for flexible probabilistic modeling with deep learning integration and TensorFlow backend.

Open-source ML and LLM observability framework for evaluating, testing, and monitoring AI-powered systems and data pipelines.

Sequence-to-sequence toolkit by Facebook AI Research for training custom models for translation, summarization, language modeling and other text generation tasks.

Library by Facebook AI for efficient similarity search and clustering of dense vectors with support for large-scale datasets.

High-level deep learning library built on PyTorch that provides practical deep learning for coders with simplified APIs and state-of-the-art results.

Open-source feature store for machine learning providing consistent feature serving for training and inference across different environments.

Enterprise-grade feature store by LinkedIn providing scalable feature engineering and serving for production machine learning systems.

Python library for automated feature engineering using Deep Feature Synthesis to create meaningful features from relational and temporal data.

State-of-the-art NLP framework built on PyTorch providing simple interface for using and combining different word and document embeddings.

Neural network library for JAX designed for flexibility, providing elegant approach to machine learning with differentiable programming.

Machine learning stack for Julia providing elegant approach to neural networks with differentiable programming and high performance computing.

Cloud-native workflow automation platform for machine learning and data processing at scale with strong reproducibility and observability.

Python library for creating interactive maps and geospatial visualizations built on Leaflet.js for machine learning and data science applications.

Python library for topic modeling, document indexing and similarity retrieval with large corpora, implementing algorithms like Word2Vec, Doc2Vec, and LDA.

Open-source library extending pandas to enable spatial operations on geometric types for geospatial data analysis and machine learning.

Suite of machine learning products by Google Cloud enabling custom model training with minimal machine learning expertise required.

Ecosystem of open-source chatbots trained on massive curated corpus of clean assistant data including code, stories, and dialogue.

Python library for building machine learning demos and web applications with simple interface creation for ML models.

Python library for data validation, documentation, and profiling helping teams maintain data quality and catch data issues early.

Programming language for controlling large language models enabling structured generation and complex reasoning workflows.

Open-source distributed machine learning platform with AutoML functionality, supporting GLM, GBM, Deep Learning, and distributed computing for big data.

Automatic machine learning platform providing feature engineering, model selection, hyperparameter tuning, and model interpretation.

End-to-end NLP framework for building search systems and question-answering applications with support for large-scale document processing.

Open-source observability platform for LLM applications providing monitoring, analytics, and optimization for AI systems.

Header-only C++ library for fast approximate nearest neighbor search using Hierarchical Navigable Small World graphs.

Data analysis and visualization library that enables building complex visualizations with minimal code through declarative approach.

Feature store and MLOps platform providing end-to-end machine learning development with feature engineering and model serving capabilities.

Open-source framework for state-of-the-art natural language processing models including BERT, GPT, RoBERTa, and thousands of pre-trained models.

Platform for hosting machine learning demos and applications with support for Gradio, Streamlit, and other frameworks.

Integrated environment for data scientists, developers and domain experts to collaboratively work with data and build ML models.

Data science platform providing end-to-end machine learning lifecycle management with real-time data processing and model serving.

Python library providing easy interface to read and write image and video files supporting various formats and backends for scientific computing.

Image augmentation library for machine learning experiments providing augmentation techniques for images, keypoints, bounding boxes, and heatmaps.

Fast Python implementation of collaborative filtering for implicit feedback datasets using matrix factorization algorithms.

Python library for structured outputs with LLMs using Pydantic models to ensure type safety and data validation.

Open-source ChatGPT alternative that runs 100% offline on your computer with support for various open-source language models.

Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning.

Lightweight pipelining library providing efficient tools for parallel computing and caching in Python, widely used in scikit-learn.

Neural network by OpenAI that generates music with singing in the raw audio domain, including rudimentary lyrics and diverse musical styles.

Machine learning framework for Julia providing consistent interface to machine learning models with emphasis on composability and flexibility.

Framework connecting data streams with ML/AI frameworks on Kubernetes, managing pipelines for TensorFlow, Keras, and PyTorch models.

Examples and patterns for deploying machine learning models in Apache Kafka Streams for real-time analytics and predictions.

Open-source Python framework for creating reproducible, maintainable and modular data science code with pipeline abstractions.

High-level neural-network API offering fast experimentation on top of TensorFlow, JAX, and PyTorch.

Deep learning framework for Julia providing automatic differentiation and GPU computing with dynamic computational graphs and high-level abstractions.

Open-source analytics platform for data science workflows with visual programming interface and extensive machine learning capabilities.

Machine learning toolkit for Kubernetes that provides end-to-end ML workflows from experimentation to production deployment on cloud platforms.

GPU cloud platform optimized for AI and machine learning providing high-performance compute with pre-configured environments.

Framework for developing applications powered by language models with tools for chaining prompts, agents, and memory management.

Open-source LLM engineering platform providing observability, analytics, and testing for language model applications.

Library for building stateful, multi-actor applications with LLMs enabling complex workflows with cycles, controllability, and persistence.

Developer platform for building, testing, and monitoring LLM applications with comprehensive tracing and evaluation capabilities.

Python library for music and audio analysis providing building blocks for music information retrieval systems and audio feature extraction.

Fast gradient boosting framework by Microsoft that uses tree-based learning algorithms with optimized memory usage and training speed.

Unified interface for calling 100+ LLMs using the OpenAI format, simplifying multi-provider LLM integration.

Port of Facebook's LLaMA model in C/C++ enabling efficient inference on commodity hardware without GPU requirements.

Data framework for LLM applications providing tools for ingesting, structuring, and accessing private or domain-specific data.

Self-hosted OpenAI-compatible API for running LLMs, audio transcription, and image generation on consumer hardware.

Declarative machine learning framework by Uber that enables training and testing deep learning models without writing code through configuration files.

Research project by Google exploring the role of machine learning as a tool in the creative process for generating music and art.

Lightweight AI toolkit for building natural language interfaces that feels like writing traditional software with AI-native functions.

Framework by Google for building perception pipelines processing time-series data like video, audio, and sensor data for mobile and desktop applications.

Human-friendly Python library by Netflix for building and managing real-life data science projects with infrastructure abstraction.

Production-grade open-source deep learning library with comprehensive support for feedforward, convolutional, and recurrent neural networks.

Open-source vector database built for scalable similarity search and AI applications with cloud-native architecture.

Deep learning framework by Huawei providing efficient development, training and deployment for AI applications across all scenarios.

Mobile SDK by Google that brings Google's machine learning expertise to Android and iOS apps with on-device and cloud-based APIs.

Friendly machine learning library for creative coding, built on top of TensorFlow.js with a focus on accessibility and ease of use.

Open-source platform for the complete machine learning lifecycle, including experiment tracking, model packaging, and deployment management.

Machine learning framework for R providing object-oriented interface to hundreds of learning algorithms with unified handling of tasks, learners, and measures.

Open source object detection toolbox based on PyTorch providing unified framework for various detection algorithms with high performance and modularity.

Foundational library by OpenMMLab for training deep learning models based on PyTorch, implementing next-generation training architecture.

Cloud platform for running generative AI models, large-scale batch jobs, and web apps with serverless GPU infrastructure.

Modular active learning framework for Python3 built on scikit-learn, designed for creating active learning workflows with flexibility and extensibility.

Distributed DataFrame library providing pandas-compatible API with automatic parallelization for faster data processing and analysis.

PyTorch-based open-source framework for deep learning in healthcare imaging providing domain-optimized capabilities for medical AI.

Python standard library module for process-based parallelism enabling CPU-intensive machine learning tasks to utilize multiple cores.

Experiment tracker purpose-built for foundation models providing comprehensive monitoring and debugging capabilities for large-scale training.

Machine learning framework providing neat pipelines for hyperparameter tuning and AutoML with clean code architecture and production deployment.

Leading platform for building Python programs to work with human language data, providing libraries for classification, tokenization, stemming, and parsing.

Non-Metric Space Library for efficient similarity search in generic non-metric spaces with support for various distance functions.

Lightweight probabilistic programming library based on NumPy and JAX providing fast and flexible Bayesian inference.

Tool for running large language models locally with simple installation and usage for offline AI applications.

Deep learning framework designed for distributed training with static graph optimization and dynamic eager execution modes.

Open-source AI ecosystem establishing standards for representing machine learning algorithms and enabling interoperability between frameworks.

Cross-platform machine learning model accelerator with flexible interface to integrate hardware-specific libraries for optimized inference.

Framework for evaluating large language models providing standardized benchmarks and evaluation protocols for AI systems.

Toolkit for developing and comparing reinforcement learning algorithms providing standardized environments and benchmarks for RL research.

Open source computer vision and machine learning library with C++, Python, and Java interfaces supporting various platforms including mobile and embedded systems.

Open-source toolkit for optimizing and deploying AI inference across Intel hardware platforms, supporting popular frameworks and edge deployment.

Automatic hyperparameter optimization framework designed for machine learning with efficient algorithms and easy-to-use API for parameter tuning.

Open-source data visualization, machine learning and data mining toolkit with visual programming interface for exploratory data analysis.

Python library for structured generation with language models providing tools for guided text generation and JSON schema compliance.

Deep learning platform by Baidu providing easy-to-use, efficient, flexible and scalable framework for industrial-grade deep learning applications.

Data integration and analytics platform with machine learning capabilities for large-scale data operations and model deployment.

Open-source framework for validating DataFrame-like objects providing flexible and expressive API for data validation with statistical typing.

High-level app and dashboarding solution for Python enabling creation of custom analytics apps from Jupyter notebooks and scripts.

Cloud platform for machine learning and AI development providing GPU instances, notebooks, and deployment solutions.

Parameter-Efficient Fine-Tuning library by Hugging Face providing methods to fine-tune large models with minimal computational resources.

Framework for building AI assistants with memory, knowledge, and tools enabling persistent and intelligent AI applications.

Python Imaging Library providing image processing capabilities including loading, manipulating, and saving images in various formats for computer vision tasks.

Fully managed vector database for high-performance AI applications providing fast and accurate similarity search at scale.

Web framework for building analytical web applications in Python with interactive visualizations for machine learning and data science.

Fast DataFrames library written in Rust with Python API providing blazing fast data processing and analysis capabilities.

Fast and flexible probabilistic modeling library implementing graphical models including Bayesian networks and hidden Markov models.

Modern workflow orchestration platform for data engineers and data scientists with dynamic, resilient, and observable data workflows.

Forecasting tool by Facebook for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

Neural building blocks for speaker diarization including voice activity detection, speaker change detection, overlapped speech detection, and speaker embedding.

Open-source low-code machine learning library that automates machine learning workflows with minimal code requirements for rapid prototyping.