AI Development AI Tools

Leading AI community platform with pre-trained models, datasets, and tools for machine learning development.

MLOps platform for experiment tracking, model management, and collaboration in machine learning projects.

Open-source platform for managing the complete machine learning lifecycle including experimentation and deployment.

Unified analytics platform for big data and machine learning with collaborative workspace features.

Metadata store for MLOps that helps manage machine learning experiments and model development.

Open-source MLOps platform for experiment management, data management, and model orchestration.

Machine learning platform for tracking, comparing, and optimizing experiments and models.

Open-source version control system for machine learning projects with data and model versioning.

Machine learning toolkit for Kubernetes that simplifies deployment of ML workflows on Kubernetes.

Unified framework for scaling AI and Python applications with distributed computing capabilities.

Open-source platform for end-to-end machine learning model development, training, and deployment.

Deep learning framework providing flexibility and speed for research and production deployment.

High-level deep learning API running on TensorFlow, simplifying model building and experimentation.

High-performance numerical computing library for machine learning research and development.

Open standard format for representing ML models and enabling interoperability between frameworks.

Intel’s toolkit for optimizing and deploying AI inference on CPU, GPU, and edge devices.

Free cloud-based Jupyter notebooks with GPU/TPU acceleration for running ML experiments.

Fully managed AWS service for building, training, and deploying machine learning models at scale.

Azure cloud service for accelerating and managing the ML project lifecycle.

Integrated environment for building, training, and managing AI and ML models on IBM Cloud.

Google Cloud’s unified platform for developing, deploying, and scaling ML models.

API for integrating GPT-class models into applications for text, chat, and embeddings.

API access to Claude models for safe, helpful AI assistants and generative applications.

LLM platform and API for text generation, classification, and embeddings.

Open and efficient LLMs with an API for text generation and embeddings.

Framework for building retrieval-augmented generation (RAG) and LLM data agents.

Framework for composing LLM-powered apps with chains, tools, and agents.

Open-source framework for building search, RAG, and question-answering systems.

Open-source framework for building conversational AI assistants.

Industrial-strength NLP library for Python with pretrained pipelines and models.

Deep learning library built on PyTorch for rapid prototyping and high accuracy.

Fast, distributed, high-performance gradient boosting framework by Microsoft.

Optimized distributed gradient boosting library for supervised learning tasks.

Gradient boosting on decision trees with strong support for categorical features.

Flexible hyperparameter optimization framework with powerful visualization tools.

Distributed hyperparameter optimization with Bayesian and random search.

Scalable hyperparameter tuning library built on Ray for distributed experiments.

Open-source deep learning training platform with distributed training and MLOps.

MLOps platform for reproducible, scalable experiments and model management.

Data versioning and pipeline orchestration for machine learning workflows.

Platform to programmatically author, schedule, and monitor workflows.

Workflow orchestration for dataflow automation with a modern Pythonic interface.

Data orchestration platform for machine learning, analytics, and ETL.

Cloud-native workflow automation and data processing platform for ML.

Human-friendly Python framework for data science and ML projects at scale.

Build, package, and deploy ML services as flexible APIs and containers.

Open-source platform for deploying and monitoring ML models on Kubernetes.

Kubernetes-native model serving for inference on top of Knative and Istio.

Model serving framework for PyTorch to deploy at scale.

High-performance inference server supporting multiple frameworks and backends.

Fast, memory-efficient inference and serving engine for LLMs.

Run large language models locally with a simple developer-friendly CLI.

Hugging Face’s optimized toolkit for high-throughput text-generation serving.

High-performance runtime for deploying ONNX models across platforms.

NVIDIA’s SDK for high-performance deep learning inference optimization.

Open deep learning compiler stack for CPUs, GPUs, and specialized accelerators.

Facebook AI’s library for efficient similarity search and clustering of vectors.

Open-source vector database for scalable similarity search and AI applications.

Cloud-native vector database with modular retrieval and GraphQL/REST APIs.

High-performance vector database optimized for semantic search and RAG.

Fully managed vector database for production-grade similarity search.

Open-source embedding database and toolkit for building RAG pipelines.

In-memory database with vector search and AI capabilities for low-latency apps.

Open-source PostgreSQL extension for efficient vector similarity search.

Open-source feature store for managing and serving ML features in production.

Enterprise feature platform to build, manage, and serve ML features online and offline.

Feature store and ML platform with real-time feature serving and governance.

Data validation framework for testing and documenting data quality in ML pipelines.

Open-source tools for ML monitoring, data drift, and model performance reports.

AI observability platform for monitoring data and model quality in production.

ML observability platform for drift detection, performance tracking, and tracing.

Responsible AI platform for model monitoring, explainability, and compliance.

Testing and validation for ML models and data across the ML lifecycle.

Open-source testing platform for ML models, including LLMs and tabular models.

LangChain’s platform for tracing, evaluating, and monitoring LLM applications.

Open-source LLM observability and analytics for prompts, traces, and evaluations.

Logging, analytics, and cost tracking for LLM API usage.

Version control, management, and analytics for prompts used in LLM apps.

Open-source framework for evaluating and monitoring LLM applications.

Evaluation framework for RAG pipelines to assess quality and correctness.

Framework for building reliable LLM apps with validation, type-safety, and policies.

Open-source ML/LLM observability and evaluation toolkit from Arize.

Deep learning optimization library for efficient large-scale training in PyTorch.

Framework for training large transformer models efficiently and at scale.

Hugging Face library for simple, distributed, mixed-precision training.

Parameter-efficient fine-tuning methods (e.g., LoRA) for adapting large models.

Automated machine learning from H2O for fast model training and selection.

Interactive computing environment combining code, equations, visualizations, and text.

Open table format bringing ACID transactions to data lakes for ML/AI.

Data lake version control for reproducible ML experiments and pipelines.

Open-source data labeling tool supporting text, images, audio, and video.

Training data platform for labeling, model-assisted labeling, and QA.

Scriptable annotation tool for creating high-quality labeled datasets fast.

Data platform for managing, labeling, and evaluating datasets for AI.

Open-source training data platform for labeling images, video, text, and more.

End-to-end data annotation platform for computer vision and NLP projects.

Text annotation tool for teams with workflow management and QA features.

Open-source annotation tool for text classification, sequence labeling, and translation.

Computer vision dataset management, labeling, and model deployment platform.

AI platform for computer vision teams—labeling, training, and deployment.