Best AI tools for< generate data models >

Qlerify is an AI-powered process and data modeling application designed to help IT transformation teams align work across silos. It enables users to create workflows, gather requirements, and turn process models into data models quickly and efficiently. Qlerify supports agile methodologies such as Event Storming, Domain Driven Design, and User Story Mapping, and offers a co-authoring workspace with AI autogeneration of content, process modeling, data modeling, requirements gathering, and backlog management.

Softbuilder is a software development company that focuses on creating innovative database tools. Their products include AbstraLinx, a powerful metadata discovery tool for Salesforce, ERBuilder Data Modeler for high-quality data models, and SB Data Generator for generating and populating databases with realistic test data. Softbuilder aims to provide straightforward tools that leverage the latest technology to enhance productivity and simplify database management.

Gretel.ai is a multimodal synthetic data platform designed for developers. It allows users to generate synthetic data from input prompts, build data pipelines, transform data using flexible rule-based methods, and evaluate the quality of synthetic data. The platform aims to improve machine learning robustness, enable safe data sharing, and power generative AI models across various industries such as finance, healthcare, and the public sector. Gretel.ai offers a comprehensive set of tools and resources, including the Gretel CLI, Blueprints, and open-source projects, to help developers create accurate and safe synthetic datasets for their AI models.

Scrol.ai is a powerful AI-powered tool that allows users to search, analyze, and generate data from various sources. It utilizes advanced language models like GPT-4 and ChatGPT to provide users with a seamless and efficient way to extract insights, summarize information, and create new content. With its user-friendly interface and robust features, Scrol.ai empowers users to streamline their workflow, enhance productivity, and make informed decisions.

FormulaBot.com is a free AI Excel Formula Generator tool that revolutionizes data work with its AI-powered data analytics features. It helps users convert text into formulas, perform analysis, create data visualizations, build advanced data models, and more. The tool is designed to streamline data-related tasks and enhance productivity for individuals and businesses alike.

Amplication is an AI-powered platform for .NET and Node.js app development, offering the world's fastest way to build backend services. It empowers developers by providing customizable, production-ready backend services without vendor lock-ins. Users can define data models, extend and customize with plugins, generate boilerplate code, and modify the generated code freely. The platform supports role-based access control, microservices architecture, continuous Git sync, and automated deployment. Amplication is SOC-2 certified, ensuring data security and compliance.

Flowscript is a revolutionary app development platform that empowers users to build fully functional web applications using plain English. Its AI-powered engine instantly converts requirements specified in plain English into applications with robust business rules, data models, and secure workflows. With Flowscript, anyone can become a developer and streamline their workflows, getting more done in less time. It offers features like AI-powered workflows, data tables with relations, dynamic form generation, engagement and interaction tools, and secure data storage options.

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.

Fine-Tune AI is a tool that allows users to generate fine-tune data sets using prompts. This can be useful for a variety of tasks, such as improving the accuracy of machine learning models or creating new training data for AI applications.

MOSTLY AI is a platform that offers the highest accuracy synthetic data generation for free. It enables true data democratization by providing privacy-safe synthetic data for various use cases such as data sharing, AI/ML development, testing & QA, and self-service analytics. The platform features a GenAI-powered assistant, natural language interface, Python client, and optimized GenAI models for tabular data. MOSTLY AI ensures privacy and compliance by creating fully anonymous synthetic data that can be easily shared internally and externally without compromising privacy regulations like GDPR and CCPA.

Incribo is a company that provides synthetic data for training machine learning models. Synthetic data is artificially generated data that is designed to mimic real-world data. This data can be used to train machine learning models without the need for real-world data, which can be expensive and difficult to obtain. Incribo's synthetic data is high quality and affordable, making it a valuable resource for machine learning developers.

Tonic.ai is a platform that allows users to build AI models on their unstructured data. It offers various products for software development and LLM development, including tools for de-identifying and subsetting structured data, scaling down data, handling semi-structured data, and managing ephemeral data environments. Tonic.ai focuses on standardizing, enriching, and protecting unstructured data, as well as validating RAG systems. The platform also provides integrations with relational databases, data lakes, NoSQL databases, flat files, and SaaS applications, ensuring secure data transformation for software and AI developers.

DataZenith is an AI application that leverages virtual reality (VR) technology to generate realistic and immersive datasets for training AI models. It enables the development of AI algorithms that can understand and interact with virtual environments, improving algorithm accuracy and performance in real-world scenarios. DataZenith offers user-friendly solutions for non-technical users, with features such as realistic VR data generation, addressing edge cases, user-friendly interface, customizable VR environments, and precise VR data annotations.

LLMStack is an open-source platform that allows users to build AI agents, workflows, and applications with their own data. It supports all major model providers, including OpenAI, Cohere, Stability AI, Hugging Face, and more. Users can easily use these models to build powerful apps and focus on what matters most. LLMStack also supports a wide variety of data sources, including Web URLs, Sitemaps, PDFs, Audio, PPTs, Google Drive, and Notion imports. This makes it easy to bring your own data and connect it to LLM models to supercharge your generative AI applications and chatbots. LLMStack is built on React, which makes it easy to build apps collaboratively. You can share apps publicly with everyone on the internet or restrict access to only certain individuals using the granular permission model. Viewer and collaborator roles allow multiple users to modify and build the app together.

Gretel.ai is an AI tool that helps users incorporate generative AI into their data by generating synthetic data that is as good or better than the existing data. Users can fine-tune custom AI models and use Gretel's APIs to generate unlimited synthesized datasets, perform privacy-preserving transformations on sensitive data, and identify PII with advanced NLP detection. Gretel's APIs make it simple to generate anonymized and safe synthetic data, allowing users to innovate faster and preserve privacy while doing it. Gretel's platform includes Synthetics, Transform, and Classify APIs that provide users with a complete set of tools to create safe data. Gretel also offers a range of resources, including documentation, tutorials, GitHub projects, and open-source SDKs for developers. Gretel Cloud runners allow users to keep data contained by running Gretel containers in their environment or scaling out workloads to the cloud in seconds. Overall, Gretel.ai is a powerful AI tool for generating synthetic data that can help users unlock innovation and achieve more with safe access to the right data.

QuarkIQL is a generative testing tool for computer vision APIs. It allows users to create custom test images and requests with just a few clicks. QuarkIQL also provides a log of your queries so you can run more experiments without starting from square one.

PFP.AI is an AI Profile Picture Generator and Maker that uses artificial intelligence to create personalized profile pictures for users. The website offers over 350 styles to choose from, catering to a wide range of preferences. Users can upload various types of photos and the AI model generates avatars based on the input, ensuring privacy by deleting all data and models within 7 days. The tool is founded by Danny Postma, a solopreneur from The Netherlands, who built the project independently without any investors, guaranteeing data security and privacy. PFP.AI provides a one-time payment option with no subscription required, making it convenient for users to create their ideal profile pictures.

Helix AI is a private GenAI platform that enables users to build AI applications using open source models. The platform offers tools for RAG (Retrieval-Augmented Generation) and fine-tuning, allowing deployment on-premises or in a Virtual Private Cloud (VPC). Users can access curated models, utilize Helix API tools to connect internal and external APIs, embed Helix Assistants into websites/apps for chatbot functionality, write AI application logic in natural language, and benefit from the innovative RAG system for Q&A generation. Additionally, users can fine-tune models for domain-specific needs and deploy securely on Kubernetes or Docker in any cloud environment. Helix Cloud offers free and premium tiers with GPU priority, catering to individuals, students, educators, and companies of varying sizes.

ImageBind by Meta AI is a groundbreaking AI tool that revolutionizes the field of computer vision by introducing a new way to 'link' AI across multiple senses. It is the first AI model capable of binding data from six different modalities simultaneously, without the need for explicit supervision. By recognizing relationships between images, video, audio, text, depth, thermal, and inertial measurement units (IMUs), ImageBind enables machines to analyze various forms of information collectively. The tool achieves emergent zero-shot recognition tasks across modalities, outperforming specialist models trained for specific modalities. ImageBind upgrades existing AI models to support input from any of the six modalities, facilitating audio-based search, cross-modal search, multimodal arithmetic, and cross-modal generation.

AdGen AI is an AI-powered creative generator that helps businesses create high-performing ad copy and visuals for multiple ad channels. It uses machine learning models to analyze product data and generate a variety of ad creatives that are tailored to the target audience. AdGen AI also allows users to publish ads directly from the platform, making it easy to launch and manage ad campaigns.

Data-Juicer is a one-stop data processing system to make data higher-quality, juicier, and more digestible for LLMs. It is a systematic & reusable library of 80+ core OPs, 20+ reusable config recipes, and 20+ feature-rich dedicated toolkits, designed to function independently of specific LLM datasets and processing pipelines. Data-Juicer allows detailed data analyses with an automated report generation feature for a deeper understanding of your dataset. Coupled with multi-dimension automatic evaluation capabilities, it supports a timely feedback loop at multiple stages in the LLM development process. Data-Juicer offers tens of pre-built data processing recipes for pre-training, fine-tuning, en, zh, and more scenarios. It provides a speedy data processing pipeline requiring less memory and CPU usage, optimized for maximum productivity. Data-Juicer is flexible & extensible, accommodating most types of data formats and allowing flexible combinations of OPs. It is designed for simplicity, with comprehensive documentation, easy start guides and demo configs, and intuitive configuration with simple adding/removing OPs from existing configs.

Distilabel is a framework for synthetic data and AI feedback for AI engineers that require high-quality outputs, full data ownership, and overall efficiency. It helps you synthesize data and provide AI feedback to improve the quality of your AI models. With Distilabel, you can: * **Synthesize data:** Generate synthetic data to train your AI models. This can help you to overcome the challenges of data scarcity and bias. * **Provide AI feedback:** Get feedback from AI models on your data. This can help you to identify errors and improve the quality of your data. * **Improve your AI output quality:** By using Distilabel to synthesize data and provide AI feedback, you can improve the quality of your AI models and get better results.

Chronon is a platform that simplifies and improves ML workflows by providing a central place to define features, ensuring point-in-time correctness for backfills, simplifying orchestration for batch and streaming pipelines, offering easy endpoints for feature fetching, and guaranteeing and measuring consistency. It offers benefits over other approaches by enabling the use of a broad set of data for training, handling large aggregations and other computationally intensive transformations, and abstracting away the infrastructure complexity of data plumbing.

Hof is a CLI tool that unifies data models, schemas, code generation, and a task engine. It allows users to augment data, config, and schemas with CUE to improve consistency, generate multiple Yaml and JSON files, explore data or config with a TUI, and run workflows with automatic task dependency inference. The tool uses CUE to power the DX and implementation, providing a language for specifying schemas, configuration, and writing declarative code. Hof offers core features like code generation, data model management, task engine, CUE cmds, creators, modules, TUI, and chat for better, scalable results.

Vodalus Expert LLM Forge is a tool designed for crafting datasets and efficiently fine-tuning models using free open-source tools. It includes components for data generation, LLM interaction, RAG engine integration, model training, fine-tuning, and quantization. The tool is suitable for users at all levels and is accompanied by comprehensive documentation. Users can generate synthetic data, interact with LLMs, train models, and optimize performance for local execution. The tool provides detailed guides and instructions for setup, usage, and customization.

DataDreamer is a powerful open-source Python library designed for prompting, synthetic data generation, and training workflows. It is simple, efficient, and research-grade, allowing users to create prompting workflows, generate synthetic datasets, and train models with ease. The library is built for researchers, by researchers, focusing on correctness, best practices, and reproducibility. It offers features like aggressive caching, resumability, support for bleeding-edge techniques, and easy sharing of datasets and models. DataDreamer enables users to run multi-step prompting workflows, generate synthetic datasets for various tasks, and train models by aligning, fine-tuning, instruction-tuning, and distilling them using existing or synthetic data.

ApiCat is an API documentation management tool that is fully compatible with the OpenAPI specification. With ApiCat, you can freely and efficiently manage your APIs. It integrates the capabilities of LLM, which not only helps you automatically generate API documentation and data models but also creates corresponding test cases based on the API content. Using ApiCat, you can quickly accomplish anything outside of coding, allowing you to focus your energy on the code itself.

This repository is an awesome list dedicated to resources for using Large Language Models (LLMs) to generate JSON or other structured outputs. It includes terminology explanations, hosted and local models, Python libraries, blog articles, videos, Jupyter notebooks, and leaderboards related to LLMs and JSON generation. The repository covers various aspects such as function calling, JSON mode, guided generation, and tool usage with different providers and models.

MMOS (Mix of Minimal Optimal Sets) is a dataset designed for math reasoning tasks, offering higher performance and lower construction costs. It includes various models and data subsets for tasks like arithmetic reasoning and math word problem solving. The dataset is used to identify minimal optimal sets through reasoning paths and statistical analysis, with a focus on QA-pairs generated from open-source datasets. MMOS also provides an auto problem generator for testing model robustness and scripts for training and inference.

LLM Datasets is a repository containing high-quality datasets, tools, and concepts for LLM fine-tuning. It provides datasets with characteristics like accuracy, diversity, and complexity to train large language models for various tasks. The repository includes datasets for general-purpose, math & logic, code, conversation & role-play, and agent & function calling domains. It also offers guidance on creating high-quality datasets through data deduplication, data quality assessment, data exploration, and data generation techniques.

Amplication is a robust, open-source development platform designed to revolutionize the creation of scalable and secure .NET and Node.js applications. It automates backend applications development, ensuring consistency, predictability, and adherence to the highest standards with code that's built to scale. The user-friendly interface fosters seamless integration of APIs, data models, databases, authentication, and authorization. Built on a flexible, plugin-based architecture, Amplication allows effortless customization of the code and offers a diverse range of integrations. With a strong focus on collaboration, Amplication streamlines team-oriented development, making it an ideal choice for groups of all sizes, from startups to large enterprises. It enables users to concentrate on business logic while handling the heavy lifting of development. Experience the fastest way to develop .NET and Node.js applications with Amplication.

The 'nlp-llms-resources' repository is a comprehensive resource list for Natural Language Processing (NLP) and Large Language Models (LLMs). It covers a wide range of topics including traditional NLP datasets, data acquisition, libraries for NLP, neural networks, sentiment analysis, optical character recognition, information extraction, semantics, topic modeling, multilingual NLP, domain-specific LLMs, vector databases, ethics, costing, books, courses, surveys, aggregators, newsletters, papers, conferences, and societies. The repository provides valuable information and resources for individuals interested in NLP and LLMs.

Instructor for PHP is a library designed for structured data extraction in PHP, powered by Large Language Models (LLMs). It simplifies the process of extracting structured, validated data from unstructured text or chat sequences. Instructor enhances workflow by providing a response model, validation capabilities, and max retries for requests. It supports classes as response models and provides features like partial results, string input, extracting scalar and enum values, and specifying data models using PHP type hints or DocBlock comments. The library allows customization of validation and provides detailed event notifications during request processing. Instructor is compatible with PHP 8.2+ and leverages PHP reflection, Symfony components, and SaloonPHP for communication with LLM API providers.

BAML is a config file format for declaring LLM functions that you can then use in TypeScript or Python. With BAML you can Classify or Extract any structured data using Anthropic, OpenAI or local models (using Ollama) ## Resources  [Discord Community](https://discord.gg/boundaryml)  [Follow us on Twitter](https://twitter.com/boundaryml) * Discord Office Hours - Come ask us anything! We hold office hours most days (9am - 12pm PST). * Documentation - Learn BAML * Documentation - BAML Syntax Reference * Documentation - Prompt engineering tips * Boundary Studio - Observability and more #### Starter projects * BAML + NextJS 14 * BAML + FastAPI + Streaming ## Motivation Calling LLMs in your code is frustrating: * your code uses types everywhere: classes, enums, and arrays * but LLMs speak English, not types BAML makes calling LLMs easy by taking a type-first approach that lives fully in your codebase: 1. Define what your LLM output type is in a .baml file, with rich syntax to describe any field (even enum values) 2. Declare your prompt in the .baml config using those types 3. Add additional LLM config like retries or redundancy 4. Transpile the .baml files to a callable Python or TS function with a type-safe interface. (VSCode extension does this for you automatically). We were inspired by similar patterns for type safety: protobuf and OpenAPI for RPCs, Prisma and SQLAlchemy for databases. BAML guarantees type safety for LLMs and comes with tools to give you a great developer experience:  Jump to BAML code or how Flexible Parsing works without additional LLM calls. | BAML Tooling | Capabilities | | ----------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | BAML Compiler install | Transpiles BAML code to a native Python / Typescript library (you only need it for development, never for releases) Works on Mac, Windows, Linux  | | VSCode Extension install | Syntax highlighting for BAML files Real-time prompt preview Testing UI | | Boundary Studio open (not open source) | Type-safe observability Labeling |

AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.

LLM4IR-Survey is a collection of papers related to large language models for information retrieval, organized according to the survey paper 'Large Language Models for Information Retrieval: A Survey'. It covers various aspects such as query rewriting, retrievers, rerankers, readers, search agents, and more, providing insights into the integration of large language models with information retrieval systems.

Starwhale is an MLOps/LLMOps platform that brings efficiency and standardization to machine learning operations. It streamlines the model development lifecycle, enabling teams to optimize workflows around key areas like model building, evaluation, release, and fine-tuning. Starwhale abstracts Model, Runtime, and Dataset as first-class citizens, providing tailored capabilities for common workflow scenarios including Models Evaluation, Live Demo, and LLM Fine-tuning. It is an open-source platform designed for clarity and ease of use, empowering developers to build customized MLOps features tailored to their needs.

This repository contains custom recipes for H2O Driverless AI, which is an Automatic Machine Learning platform for the Enterprise. Custom recipes are Python code snippets that can be uploaded into Driverless AI at runtime to automate feature engineering, model building, visualization, and interpretability. Users can gain control over the optimization choices made by Driverless AI by providing their own custom recipes. The repository includes recipes for various tasks such as data manipulation, data preprocessing, feature selection, data augmentation, model building, scoring, and more. Best practices for creating and using recipes are also provided, including security considerations, performance tips, and safety measures.

ChainForge is a visual programming environment for battle-testing prompts to LLMs. It is geared towards early-stage, quick-and-dirty exploration of prompts, chat responses, and response quality that goes beyond ad-hoc chatting with individual LLMs. With ChainForge, you can: * Query multiple LLMs at once to test prompt ideas and variations quickly and effectively. * Compare response quality across prompt permutations, across models, and across model settings to choose the best prompt and model for your use case. * Setup evaluation metrics (scoring function) and immediately visualize results across prompts, prompt parameters, models, and model settings. * Hold multiple conversations at once across template parameters and chat models. Template not just prompts, but follow-up chat messages, and inspect and evaluate outputs at each turn of a chat conversation. ChainForge comes with a number of example evaluation flows to give you a sense of what's possible, including 188 example flows generated from benchmarks in OpenAI evals. This is an open beta of Chainforge. We support model providers OpenAI, HuggingFace, Anthropic, Google PaLM2, Azure OpenAI endpoints, and Dalai-hosted models Alpaca and Llama. You can change the exact model and individual model settings. Visualization nodes support numeric and boolean evaluation metrics. ChainForge is built on ReactFlow and Flask.

是一个专门用于建立数学模型并撰写python代码的gpt.

A dual-expert in Pydantic V1 and V2, adept at online research for V2.

Professionally finds top custom GPT models for specific topics

Data science co-pilot specializing in statistical modeling and machine learning.

GPT Live Directory for storing and retrieving GPT models.

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Friendly, professional data analysis expert.

Analyzes data to influence strategic decision-making processes.

Leading Big Data Analytics tool, blending advanced technology with OpenAI's expertise.

A friendly data science coach offering practical, useful, and accurate advice.

Expert in Marian NMT models and training

Expert in designing GPT models and translating user needs into technical specs.

💲Monetize your new or existing GPTs! 💳Choose from free trial, freemium or premium pricing models. 🔐Generate and verify keys. 📦Self contained w/ no need for apis or actions. ✨Instant access to updates. 💾Worry free backups ⏱Save time and effort. 💰Monetize today! -v0.60

Advisor on creating and optimizing GPT models.

Discover the perfect GPTs tailored just for you from an astounding selection of 89527 models! Dive in and enjoy the magic! The GPT repository will update continuously!

Simplifies data science for professionals.

Automates DCF Analysis with internet-sourced data and summary table.

A software architect for C4 Model generation.

Expert en optimisation de performance et traitement de données

从Python基础到AI领域的导师