Best AI tools for< Create Data Model >

KNIME is a data science platform that enables users to analyze, blend, transform, model, visualize, and deploy data science solutions without coding. It provides a range of features and advantages for business and domain experts, data experts, end users, and MLOps & IT professionals across various industries and departments.

FareTrack is an AI-driven data intelligence solution tailored for the modern air travel industry. It offers accurate, timely, and actionable insights for airline revenue management, distribution, and network operations teams. By leveraging advanced AI technology, FareTrack empowers clients with competitive fare tracking, ancillary pricing insights, open pricing monitoring, and price rank value optimization. The platform also provides comprehensive travel data solutions beyond airfare, including tax breakdowns, historical fare analysis, and trend analysis. With customizable dashboards and API integration, FareTrack enables users to make informed decisions swiftly and stay ahead in the dynamic world of air travel.

Softbuilder is a software development company that focuses on creating innovative database tools. Their products include AbstraLinx, a powerful tool for Salesforce metadata exploration, ERBuilder Data Modeler for high-quality data models, and SB Data Generator for generating realistic test data. Softbuilder aims to provide straightforward tools using the latest technology to help users be more productive and focus on delivering solutions rather than learning complicated tools.

Accio is a data modeling tool that allows users to define consistent relationships, metrics, and expressions for on-the-fly computations in reports and dashboards across various BI tools. It provides a syntax similar to GraphQL that allows users to define models, relationships, and metrics in a human-readable format. Accio also offers a user-friendly interface that provides data analysts with a holistic view of the relationships between their data models, enabling them to grasp the interconnectedness and dependencies within their data ecosystem. Additionally, Accio utilizes DuckDB as a caching layer to accelerate query performance for BI tools.

Eraser AI is an AI-powered copilot for technical design, offering an all-in-one markdown editor, collaborative canvas, and diagram-as-code builder. It enables engineering teams to create diagrams, design docs, and documentation collaboratively and efficiently. With features like robust file management, blazing fast file search, wikilinks & mentions, and GitHub integration, Eraser AI streamlines the process of visualizing infrastructure, data models, process flows, and logic flows. It provides a minimal tool design for maintaining flow and focus, version history for safety, and performance optimization for distraction-free work. Additionally, Eraser AI offers workflow integration, markdown formatting, export capabilities, and enterprise-grade security features, making it a trusted tool for technical teams worldwide.

Rerun is an SDK, time-series database, and visualizer for temporal and multimodal data. It is used in fields like robotics, spatial computing, 2D/3D simulation, and finance to verify, debug, and explain data. Rerun allows users to log data like tensors, point clouds, and text to create streams, visualize and interact with live and recorded streams, build layouts, customize visualizations, and extend data and UI functionalities. The application provides a composable data model, dynamic schemas, and custom views for enhanced data visualization and analysis.

ChatDBT is a DBT designer with prompting that helps you write better DBT code. It provides a user-friendly interface that makes it easy to create and edit DBT models, and it includes a number of features that can help you improve the quality of your code.

Tresata is an AI tool that offers inventory and cataloging, inferencing and connecting, discoverability and lineage tracking, tokenization, and data enrichment capabilities. It provides SAM (Smart Augmented Intelligence) features and seamless integrations for customers. The platform empowers users to create data products for AI applications by uploading data to the Tresata cloud and accessing it for analysis and insights. Tresata emphasizes the importance of good data for all, with a focus on data-driven decision-making and innovation.

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.

Lexset is an AI tool that provides synthetic data generation services for computer vision model training. It offers a no-code interface to create unlimited data with advanced camera controls and lighting options. Users can simulate AI-scale environments, composite objects into images, and create custom 3D scenarios. Lexset also provides access to GPU nodes, dedicated support, and feature development assistance. The tool aims to improve object detection accuracy and optimize generalization on high-quality synthetic data.

Datagen is a platform that provides synthetic data for computer vision. Synthetic data is artificially generated data that can be used to train machine learning models. Datagen's data is generated using a variety of techniques, including 3D modeling, computer graphics, and machine learning. The company's data is used by a variety of industries, including automotive, security, smart office, fitness, cosmetics, and facial applications.

Julius AI is an advanced AI data analyst tool that allows users to analyze data with computational AI, chat with files to get expert-level insights, create sleek data visualizations, perform modeling and predictive forecasting, solve math, physics, and chemistry problems, generate polished analyses and summaries, save time by automating data work, and unlock statistical modeling without complexity. It offers features like generating visualizations, asking data questions, effortless cleaning, instant data export, creating animations, and supercharging data analysis. Julius AI is loved by over 1,200,000 users worldwide and is designed to help knowledge workers make the most out of their data.

Avanzai is an AI tool designed for financial services, providing intelligent automation to asset managers. It streamlines operations, enhances decision-making, and transforms data into actionable strategies. With AI-powered reports, automated portfolio management, data connectivity, and customizable agents, Avanzai empowers financial firms to optimize portfolios and make informed decisions.

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.

Breadcrumbs is a revenue acceleration platform that helps businesses optimize their entire sales and marketing funnel. It provides enterprise-grade lead scoring, allowing businesses to identify and prioritize their most promising leads. Breadcrumbs also offers a range of other features, such as data-driven model creation, unlimited workspaces and models, multi-variate testing, and integrations with a variety of marketing and sales tools. With Breadcrumbs, businesses can improve their lead quality, increase conversion rates, and accelerate revenue growth.

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.

Arcee AI is a platform that offers a cost-effective, secure, end-to-end solution for building and deploying Small Language Models (SLMs). It allows users to merge and train custom language models by leveraging open source models and their own data. The platform is known for its Model Merging technique, which combines the power of pre-trained Large Language Models (LLMs) with user-specific data to create high-performing models across various industries.

Ragobble is an audio to LLM data tool that allows you to easily convert audio files into text data that can be used to train large language models (LLMs). With Ragobble, you can quickly and easily create high-quality training data for your LLM projects.

TrainMyAI is a comprehensive solution for creating AI chatbots using retrieval augmented generation (RAG) technology. It allows users to build custom AI chatbots on their servers, enabling interactions over WhatsApp, web, and private APIs. The platform offers deep customization options, fine-grained user management, usage history tracking, content optimization, and linked citations. With TrainMyAI, users can maintain full control over their AI models and data, either on-premise or in the cloud.

Lumina is a research tool that uses artificial intelligence to help researchers find and analyze information more quickly and easily. It can be used to search for articles, books, and other resources, and it can also be used to analyze data and create visualizations. Lumina is designed to make research more efficient and productive.

Neo4j Runway is a Python library that simplifies the process of migrating relational data into a graph. It provides tools to abstract communication with OpenAI for data discovery, generate data models, ingestion code, and load data into a Neo4j instance. The library leverages OpenAI LLMs for insights, Instructor Python library for modeling, and PyIngest for data loading. Users can visualize data models using graphviz and benefit from a seamless integration with Neo4j for efficient data migration.

Ezdata is a data processing and task scheduling system developed based on Python backend and Vue3 frontend. It supports managing multiple data sources, abstracting various data sources into a unified data model, integrating chatgpt for data question and answer functionality, enabling low-code data integration and visualization processing, scheduling single and dag tasks, and integrating a low-code data visualization dashboard system.

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

Co-LLM (Collaborative Language Models) is a tool for learning to decode collaboratively with multiple language models. It provides a method for data processing, training, and inference using a collaborative approach. The tool involves steps such as formatting/tokenization, scoring logits, initializing Z vector, deferral training, and generating results using multiple models. Co-LLM supports training with different collaboration pairs and provides baseline training scripts for various models. In inference, it uses 'vllm' services to orchestrate models and generate results through API-like services. The tool is inspired by allenai/open-instruct and aims to improve decoding performance through collaborative learning.

Auto Data is a library designed for the automatic generation of realistic datasets, essential for the fine-tuning of Large Language Models (LLMs). This highly efficient and lightweight library enables the swift and effortless creation of comprehensive datasets across various topics, regardless of their size. It addresses challenges encountered during model fine-tuning due to data scarcity and imbalance, ensuring models are trained with sufficient examples.

Codefuse-ModelCache is a semantic cache for large language models (LLMs) that aims to optimize services by introducing a caching mechanism. It helps reduce the cost of inference deployment, improve model performance and efficiency, and provide scalable services for large models. The project caches pre-generated model results to reduce response time for similar requests and enhance user experience. It integrates various embedding frameworks and local storage options, offering functionalities like cache-writing, cache-querying, and cache-clearing through RESTful API. The tool supports multi-tenancy, system commands, and multi-turn dialogue, with features for data isolation, database management, and model loading schemes. Future developments include data isolation based on hyperparameters, enhanced system prompt partitioning storage, and more versatile embedding models and similarity evaluation algorithms.

Awesome MLOps is a curated list of tools related to Machine Learning Operations, covering areas such as AutoML, CI/CD for Machine Learning, Data Cataloging, Data Enrichment, Data Exploration, Data Management, Data Processing, Data Validation, Data Visualization, Drift Detection, Feature Engineering, Feature Store, Hyperparameter Tuning, Knowledge Sharing, Machine Learning Platforms, Model Fairness and Privacy, Model Interpretability, Model Lifecycle, Model Serving, Model Testing & Validation, Optimization Tools, Simplification Tools, Visual Analysis and Debugging, and Workflow Tools. The repository provides a comprehensive collection of tools and resources for individuals and teams working in the field of MLOps.

Codefuse-ModelCache is a semantic cache for large language models (LLMs) that aims to optimize services by introducing a caching mechanism. It helps reduce the cost of inference deployment, improve model performance and efficiency, and provide scalable services for large models. The project facilitates sharing and exchanging technologies related to large model semantic cache through open-source collaboration.

Cognita is an open-source framework to organize your RAG codebase along with a frontend to play around with different RAG customizations. It provides a simple way to organize your codebase so that it becomes easy to test it locally while also being able to deploy it in a production ready environment. The key issues that arise while productionizing RAG system from a Jupyter Notebook are: 1. **Chunking and Embedding Job** : The chunking and embedding code usually needs to be abstracted out and deployed as a job. Sometimes the job will need to run on a schedule or be trigerred via an event to keep the data updated. 2. **Query Service** : The code that generates the answer from the query needs to be wrapped up in a api server like FastAPI and should be deployed as a service. This service should be able to handle multiple queries at the same time and also autoscale with higher traffic. 3. **LLM / Embedding Model Deployment** : Often times, if we are using open-source models, we load the model in the Jupyter notebook. This will need to be hosted as a separate service in production and model will need to be called as an API. 4. **Vector DB deployment** : Most testing happens on vector DBs in memory or on disk. However, in production, the DBs need to be deployed in a more scalable and reliable way. Cognita makes it really easy to customize and experiment everything about a RAG system and still be able to deploy it in a good way. It also ships with a UI that makes it easier to try out different RAG configurations and see the results in real time. You can use it locally or with/without using any Truefoundry components. However, using Truefoundry components makes it easier to test different models and deploy the system in a scalable way. Cognita allows you to host multiple RAG systems using one app. ### Advantages of using Cognita are: 1. A central reusable repository of parsers, loaders, embedders and retrievers. 2. Ability for non-technical users to play with UI - Upload documents and perform QnA using modules built by the development team. 3. Fully API driven - which allows integration with other systems. > If you use Cognita with Truefoundry AI Gateway, you can get logging, metrics and feedback mechanism for your user queries. ### Features: 1. Support for multiple document retrievers that use `Similarity Search`, `Query Decompostion`, `Document Reranking`, etc 2. Support for SOTA OpenSource embeddings and reranking from `mixedbread-ai` 3. Support for using LLMs using `Ollama` 4. Support for incremental indexing that ingests entire documents in batches (reduces compute burden), keeps track of already indexed documents and prevents re-indexing of those docs.

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.

Swarms provides simple, reliable, and agile tools to create your own Swarm tailored to your specific needs. Currently, Swarms is being used in production by RBC, John Deere, and many AI startups.

MindsDB is a platform for customizing AI from enterprise data. You can create, serve, and fine-tune models in real-time from your database, vector store, and application data. MindsDB "enhances" SQL syntax with AI capabilities to make it accessible for developers worldwide. With MindsDB’s nearly 200 integrations, any developer can create AI customized for their purpose, faster and more securely. Their AI systems will constantly improve themselves — using companies’ own data, in real-time.

Prompt Panel is a tool designed to accelerate the adoption of AI agents by providing a platform where users can run large language models across any inference provider, create custom agent plugins, and use their own data safely. The tool allows users to break free from walled-gardens and have full control over their models, conversations, and logic. With Prompt Panel, users can pair their data with any language model, online or offline, and customize the system to meet their unique business needs without any restrictions.

gollm is a Go package designed to simplify interactions with Large Language Models (LLMs) for AI engineers and developers. It offers a unified API for multiple LLM providers, easy provider and model switching, flexible configuration options, advanced prompt engineering, prompt optimization, memory retention, structured output and validation, provider comparison tools, high-level AI functions, robust error handling and retries, and extensible architecture. The package enables users to create AI-powered golems for tasks like content creation workflows, complex reasoning tasks, structured data generation, model performance analysis, prompt optimization, and creating a mixture of agents.

Basdonax AI RAG v1.0 is a repository that contains all the necessary resources to create your own AI-powered secretary using the RAG from Basdonax AI. It leverages open-source models from Meta and Microsoft, namely 'Llama3-7b' and 'Phi3-4b', allowing users to upload documents and make queries. This tool aims to simplify life for individuals by harnessing the power of AI. The installation process involves choosing between different data models based on GPU capabilities, setting up Docker, pulling the desired model, and customizing the assistant prompt file. Once installed, users can access the RAG through a local link and enjoy its functionalities.

HuggingFists is a low-code data flow tool that enables convenient use of LLM and HuggingFace models. It provides functionalities similar to Langchain, allowing users to design, debug, and manage data processing workflows, create and schedule workflow jobs, manage resources environment, and handle various data artifact resources. The tool also offers account management for users, allowing centralized management of data source accounts and API accounts. Users can access Hugging Face models through the Inference API or locally deployed models, as well as datasets on Hugging Face. HuggingFists supports breakpoint debugging, branch selection, function calls, workflow variables, and more to assist users in developing complex data processing workflows.

RD-Agent is a tool designed to automate critical aspects of industrial R&D processes, focusing on data-driven scenarios to streamline model and data development. It aims to propose new ideas ('R') and implement them ('D') automatically, leading to solutions of significant industrial value. The tool supports scenarios like Automated Quantitative Trading, Data Mining Agent, Research Copilot, and more, with a framework to push the boundaries of research in data science. Users can create a Conda environment, install the RDAgent package from PyPI, configure GPT model, and run various applications for tasks like quantitative trading, model evolution, medical prediction, and more. The tool is intended to enhance R&D processes and boost productivity in industrial settings.

Azure OpenAI Service provides REST API access to OpenAI's powerful language models including GPT-3, Codex and Embeddings. Users can easily adapt models for content generation, summarization, semantic search, and natural language to code translation. The workshop covers basics, prompt engineering, common NLP tasks, generative tasks, conversational dialog, and learning methods. It guides users to build applications with PowerApp, query SQL data, create data pipelines, and work with proprietary datasets. Target audience includes Power Users, Software Engineers, Data Scientists, and AI architects and Managers.

“Data Deep Dive”: This is an expert AI tool for Excel and Power BI. Get expert help with DAX, Power Query, VBA, data models, and visualizations. Ideal for all levels: from basic functions to advanced analytics.

Convert application descriptions to consumable DB schemas or create-table SQL statements

A detailed expert trained on all 5,588 pages of Apple CoreData, offering complete coding solutions. Saving time? https://www.buymeacoffee.com/parkerrex ☕️❤️

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

Expert in teaching Pydantic with clear, accessible explanations.

Provides optimized, commented Python code.

Takes in a Project Design Document and generates a database schema diagram for the project.

This GPT can read all Streamlit Documantation and helps you about Streamlit.

Your purpose is to create the pytorch code to train language models using pytorch

The IlluminatiAI model represents a novel approach in the field of artificial intelligence, incorporating elements of secret societies, ancient knowledge, and hidden wisdom into its algorithms.

Microsoft Power Platform Expert GPT, Modeled on the Knowledge and Personality of Steve Mordue MVP.

Expert in financial modeling for valuation, budgeting, and forecasting.

I create personalized code templates for your projects.

This tool is designed to locate the ideal GPT model tailored to your specific requirements. Simply articulate your needs, and it will diligently work to identify the perfect GPT solution for you.

💲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

FinWiz-GPT is designed for finance professionals. It assists in market analysis, financial modeling, and understanding complex financial instruments. It's a great tool for financial analysts, investment bankers, and accountants.

I create and explain ARIS diagrams.

Create personalized GPTs with my simple builder. Click the conversation starter (starting with ###) to begin.

Your wise R Shiny sage, offering enlightened guidance.

I'm here to make your Streamlit apps go viral!