Best AI tools for< Label Data Efficiently >

Encord is a leading data development platform designed for computer vision and multimodal AI teams. It offers a comprehensive suite of tools to manage, clean, and curate data, streamline labeling and workflow management, and evaluate AI model performance. With features like data indexing, annotation, and active model evaluation, Encord empowers users to accelerate their AI data workflows and build robust models efficiently.

AIxBlock is an AI tool that empowers users to unleash their AI initiatives on the Blockchain. The platform offers a comprehensive suite of features for building, deploying, and monitoring AI models, including AI data engine, multimodal-powered data crawler, auto annotation, consensus-driven labeling, MLOps platform, decentralized marketplaces, and more. By harnessing the power of blockchain technology, AIxBlock provides cost-efficient solutions for AI builders, compute suppliers, and freelancers to collaborate and benefit from decentralized supercomputing, P2P transactions, and consensus mechanisms.

V7 is an AI data engine for computer vision and generative AI. It provides a multimodal automation tool that helps users label data 10x faster, power AI products via API, build AI + human workflows, and reach 99% AI accuracy. V7's platform includes features such as automated annotation, DICOM annotation, dataset management, model management, image annotation, video annotation, document processing, and labeling services.

Labelbox is a data factory platform that empowers AI teams to manage data labeling, train models, and create better data with internet scale RLHF platform. It offers an all-in-one solution comprising tooling and services powered by a global community of domain experts. Labelbox operates a global data labeling infrastructure and operations for AI workloads, providing expert human network for data labeling in various domains. The platform also includes AI-assisted alignment for maximum efficiency, data curation, model training, and labeling services. Customers achieve breakthroughs with high-quality data through Labelbox.

UBIAI is a powerful text annotation tool that helps businesses accelerate their data labeling process. With UBIAI, businesses can annotate any type of document, including PDFs, images, and text. UBIAI also offers a variety of features to make the annotation process easier and more efficient, such as auto-labeling, multi-lingual annotation, and team collaboration. With UBIAI, businesses can save time and money on their data labeling projects.

madebymachines is an AI tool designed to assist users in various stages of the machine learning workflow, from data preparation to model development. The tool offers services such as data collection, data labeling, model training, hyperparameter tuning, and transfer learning. With a user-friendly interface and efficient algorithms, madebymachines aims to streamline the process of building machine learning models for both beginners and experienced users.

PromptLoop is an AI-powered tool that integrates with Excel and Google Sheets to enhance market research and data analysis. It offers custom AI models tailored to specific needs, enabling users to extract insights from complex information. With PromptLoop, users can leverage advanced AI capabilities for tasks such as web research, content analysis, and data labeling, streamlining workflows and improving efficiency.

Sylph AI is an AI tool designed to maximize the potential of LLM applications by providing an auto-optimization library and an AI teammate to assist users in navigating complex LLM workflows. The tool aims to streamline the process of model fine-tuning, hyperparameter optimization, and auto-data labeling for LLM projects, ultimately enhancing productivity and efficiency for users.

Datasaur is an advanced text and audio data labeling platform that offers customizable solutions for various industries such as LegalTech, Healthcare, Financial, Media, e-Commerce, and Government. It provides features like configurable annotation, quality control automation, and workforce management to enhance the efficiency of NLP and LLM projects. Datasaur prioritizes data security with military-grade practices and offers seamless integrations with AWS and other technologies. The platform aims to streamline the data labeling process, allowing engineers to focus on creating high-quality models.

OpenTrain AI is a data labeling marketplace that leverages artificial intelligence to streamline the process of labeling data for machine learning models. It provides a platform where users can crowdsource data labeling tasks to a global community of annotators, ensuring high-quality labeled datasets for training AI algorithms. With advanced AI algorithms and human-in-the-loop validation, OpenTrain AI offers efficient and accurate data labeling services for various industries such as autonomous vehicles, healthcare, and natural language processing.

Keylabs is a state-of-the-art data annotation platform that enhances AI projects with highly precise data annotation and innovative tools. It offers image and video annotation, labeling, and ML-assisted features for industries such as automotive, aerial, agriculture, robotics, manufacturing, waste management, medical, healthcare, retail, fashion, sports, security, livestock, construction, and logistics. Keylabs provides advanced annotation tools, built-in machine learning, efficient operation management, and extra high performance to boost the preparation of visual data for machine learning. The platform ensures transparency in pricing with no hidden fees and offers a free trial for users to experience its capabilities.

Cogniroot is an AI-powered platform that helps businesses automate their data annotation and data labeling processes. It provides a suite of tools and services that make it easy for businesses to train their machine learning models with high-quality data. Cogniroot's platform is designed to be scalable, efficient, and cost-effective, making it a valuable tool for businesses of all sizes.

FuseBase (Formerly Nimbus) is an online collaboration tool designed for businesses to enhance team collaboration and streamline project management. The platform offers features such as client collaboration, project management, knowledge management, data collection, and white-label solutions. FuseBase also integrates AI technology to provide users with an AI assistant for CRM, sales, marketing, and step-by-step guides. The application aims to improve client interactions, project efficiency, and overall business productivity by offering a centralized workspace for seamless collaboration and communication.

Snorkel AI is a data-centric AI application designed for enterprise use. It offers tools and platforms to programmatically label and curate data, accelerate AI development, and build high-quality generative AI applications. The application aims to help users develop AI models 100x faster by leveraging programmatic data operations and domain knowledge. Snorkel AI is known for its expertise in computer vision, data labeling, generative AI, and enterprise AI solutions. It provides resources, case studies, and research papers to support users in their AI development journey.

Clickworker GmbH is an AI training data and data management services platform that leverages a global crowd of Clickworkers to generate, validate, and label data for AI systems. The platform offers a range of AI datasets for machine learning, audio, image, and video datasets, as well as services like image annotation, content editing, and creation. Clickworkers participate in projects on a freelance basis, performing micro-tasks to create high-quality training data tailored to the requirements of AI systems. The platform also provides solutions for industries such as AI and data science research, eCommerce, fashion, retail, and digital marketing.

Rendered.ai is a platform that provides unlimited synthetic data for AI and ML applications, specifically focusing on computer vision. It helps in generating low-cost physically-accurate data to overcome bias and power innovation in AI and ML. The platform allows users to capture rare events and edge cases, acquire data that is difficult to obtain, overcome data labeling challenges, and simulate restricted or high-risk scenarios. Rendered.ai aims to revolutionize the use of synthetic data in AI and data analytics projects, with a vision that by 2030, synthetic data will surpass real data in AI models.

CloudFactory's AI Data Platform, including the GenAI Model Oversight Platform, integrates Hasty as a powerful tool for computer vision annotation and model development. Hasty's annotation capabilities enhance AI-driven workflows within the platform, offering comprehensive solutions for data labeling, computer vision, NLP, and more.

Synthesis AI is a synthetic data platform that enables more capable and ethical computer vision AI. It provides on-demand labeled images and videos, photorealistic images, and 3D generative AI to help developers build better models faster. Synthesis AI's products include Synthesis Humans, which allows users to create detailed images and videos of digital humans with rich annotations; Synthesis Scenarios, which enables users to craft complex multi-human simulations across a variety of environments; and a range of applications for industries such as ID verification, automotive, avatar creation, virtual fashion, AI fitness, teleconferencing, visual effects, and security.

Anote is a human-centered AI company that provides a suite of products and services to help businesses improve their data quality and build better AI models. Anote's products include a data labeler, a private chatbot, a model inference API, and a lead generation tool. Anote's services include data annotation, model training, and consulting.

Surge AI is a data labeling platform that provides human-generated data for training and evaluating large language models (LLMs). It offers a global workforce of annotators who can label data in over 40 languages. Surge AI's platform is designed to be easy to use and integrates with popular machine learning tools and frameworks. The company's customers include leading AI companies, research labs, and startups.

Phospho is a text analytics platform for LLM apps. It helps you detect issues and extract insights from text messages of your users or your app. You can gather user feedback, measure success, and iterate on your app to create the best conversational experience for your users.

Laminar is an all-in-one open-source platform designed for engineering AI products. It allows users to trace, evaluate, label, and analyze LLM data efficiently. The platform offers features such as automatic tracing of common AI frameworks and SDKs, local and online evaluations, simple UI for data labeling, dataset management, and scalability with gRPC communication. Laminar is built with a modern open-source stack including RabbitMQ, Postgres, Clickhouse, and Qdrant for semantic similarity search. It provides fast and beautiful dashboards for traces, evaluations, and labels, making it a comprehensive tool for AI product development.

Awesome-Segment-Anything is a powerful tool for segmenting and extracting information from various types of data. It provides a user-friendly interface to easily define segmentation rules and apply them to text, images, and other data formats. The tool supports both supervised and unsupervised segmentation methods, allowing users to customize the segmentation process based on their specific needs. With its versatile functionality and intuitive design, Awesome-Segment-Anything is ideal for data analysts, researchers, content creators, and anyone looking to efficiently extract valuable insights from complex datasets.

A comprehensive repository of resources for Chinese large language models (LLMs), including data processing tools, fine-tuning frameworks, inference libraries, evaluation platforms, RAG engines, agent frameworks, books, courses, tutorials, and tips. The repository covers a wide range of tools and resources for working with LLMs, from data labeling and processing to model fine-tuning, inference, evaluation, and application development. It also includes resources for learning about LLMs through books, courses, and tutorials, as well as insights and strategies from building with LLMs.

Cleanlab helps you **clean** data and **lab** els by automatically detecting issues in a ML dataset. To facilitate **machine learning with messy, real-world data** , this data-centric AI package uses your _existing_ models to estimate dataset problems that can be fixed to train even _better_ models.

EDA GPT is an open-source data analysis companion that offers a comprehensive solution for structured and unstructured data analysis. It streamlines the data analysis process, empowering users to explore, visualize, and gain insights from their data. EDA GPT supports analyzing structured data in various formats like CSV, XLSX, and SQLite, generating graphs, and conducting in-depth analysis of unstructured data such as PDFs and images. It provides a user-friendly interface, powerful features, and capabilities like comparing performance with other tools, analyzing large language models, multimodal search, data cleaning, and editing. The tool is optimized for maximal parallel processing, searching internet and documents, and creating analysis reports from structured and unstructured data.

MATLAB-Simulink-Challenge-Project-Hub is a repository aimed at contributing to the progress of engineering and science by providing challenge projects with real industry relevance and societal impact. The repository offers a wide range of projects covering various technology trends such as Artificial Intelligence, Autonomous Vehicles, Big Data, Computer Vision, and Sustainability. Participants can gain practical skills with MATLAB and Simulink while making a significant contribution to science and engineering. The projects are designed to enhance expertise in areas like Sustainability and Renewable Energy, Control, Modeling and Simulation, Machine Learning, and Robotics. By participating in these projects, individuals can receive official recognition for their problem-solving skills from technology leaders at MathWorks and earn rewards upon project completion.

This repository is a curated list of papers related to 3D tasks empowered by Large Language Models (LLMs). It covers tasks such as 3D understanding, reasoning, generation, and embodied agents. The repository also includes other Foundation Models like CLIP and SAM to provide a comprehensive view of the area. It is actively maintained and updated to showcase the latest advances in the field. Users can find a variety of research papers and projects related to 3D tasks and LLMs in this repository.

LitData is a tool designed for blazingly fast, distributed streaming of training data from any cloud storage. It allows users to transform and optimize data in cloud storage environments efficiently and intuitively, supporting various data types like images, text, video, audio, geo-spatial, and multimodal data. LitData integrates smoothly with frameworks such as LitGPT and PyTorch, enabling seamless streaming of data to multiple machines. Key features include multi-GPU/multi-node support, easy data mixing, pause & resume functionality, support for profiling, memory footprint reduction, cache size configuration, and on-prem optimizations. The tool also provides benchmarks for measuring streaming speed and conversion efficiency, along with runnable templates for different data types. LitData enables infinite cloud data processing by utilizing the Lightning.ai platform to scale data processing with optimized machines.

Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency. It helps users improve AI output quality through data quality, take control of their data and models, and improve efficiency by quickly iterating on the right data and models. Argilla is an open-source community-driven project that provides tools for achieving and maintaining high-quality data standards, with a focus on NLP and LLMs. It is used by AI teams from companies like the Red Cross, Loris.ai, and Prolific to improve the quality and efficiency of AI projects.

x-crawl is a flexible Node.js AI-assisted crawler library that offers powerful AI assistance functions to make crawler work more efficient, intelligent, and convenient. It consists of a crawler API and various functions that can work normally even without relying on AI. The AI component is currently based on a large AI model provided by OpenAI, simplifying many tedious operations. The library supports crawling dynamic pages, static pages, interface data, and file data, with features like control page operations, device fingerprinting, asynchronous sync, interval crawling, failed retry handling, rotation proxy, priority queue, crawl information control, and TypeScript support.

The vscode-pddl extension provides comprehensive support for Planning Domain Description Language (PDDL) in Visual Studio Code. It enables users to model planning domains, validate them, industrialize planning solutions, and run planners. The extension offers features like syntax highlighting, auto-completion, plan visualization, plan validation, plan happenings evaluation, search debugging, and integration with Planning.Domains. Users can create PDDL files, run planners, visualize plans, and debug search algorithms efficiently within VS Code.

Cyclops is a toolkit for facilitating research and deployment of ML models for healthcare. It provides a few high-level APIs namely: data - Create datasets for training, inference and evaluation. We use the popular 🤗 datasets to efficiently load and slice different modalities of data models - Use common model implementations using scikit-learn and PyTorch tasks - Use common ML task formulations such as binary classification or multi-label classification on tabular, time-series and image data evaluate - Evaluate models on clinical prediction tasks monitor - Detect dataset shift relevant for clinical use cases report - Create model report cards for clinical ML models

DuoAttention is a framework designed to optimize long-context large language models (LLMs) by reducing memory and latency during inference without compromising their long-context abilities. It introduces a concept of Retrieval Heads and Streaming Heads to efficiently manage attention across tokens. By applying a full Key and Value (KV) cache to retrieval heads and a lightweight, constant-length KV cache to streaming heads, DuoAttention achieves significant reductions in memory usage and decoding time for LLMs. The framework uses an optimization-based algorithm with synthetic data to accurately identify retrieval heads, enabling efficient inference with minimal accuracy loss compared to full attention. DuoAttention also supports quantization techniques for further memory optimization, allowing for decoding of up to 3.3 million tokens on a single GPU.

The Eureka ML Insights Framework is a repository containing code designed to help researchers and practitioners run reproducible evaluations of generative models efficiently. Users can define custom pipelines for data processing, inference, and evaluation, as well as utilize pre-defined evaluation pipelines for key benchmarks. The framework provides a structured approach to conducting experiments and analyzing model performance across various tasks and modalities.

Bugbug is a tool developed by Mozilla that leverages machine learning techniques to assist with bug and quality management, as well as other software engineering tasks like test selection and defect prediction. It provides various classifiers to suggest assignees, detect patches likely to be backed-out, classify bugs, assign product/components, distinguish between bugs and feature requests, detect bugs needing documentation, identify invalid issues, verify bugs needing QA, detect regressions, select relevant tests, track bugs, and more. Bugbug can be trained and tested using Python scripts, and it offers the ability to run model training tasks on Taskcluster. The project structure includes modules for data mining, bug/commit feature extraction, model implementations, NLP utilities, label handling, bug history playback, and GitHub issue retrieval.

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