Best AI tools for< Test Data >

The Education Data Center (EDC) Version 2.0 is a platform dedicated to providing clear and timely access to education data for researchers and education stakeholders. It offers a State Assessment Data Repository, a leading database of state assessment data in the United States. Users can download data files, utilize a custom-made AI tool to query the data, and access information about the EDC. The platform aims to support evidence-based decision-making to enhance the educational support for the nation's students.

Snaplet is a data management tool for developers that provides AI-generated dummy data for local development, end-to-end testing, and debugging. It uses a real programming language (TypeScript) to define and edit data, ensuring type safety and auto-completion. Snaplet understands database structures and relationships, automatically transforming personally identifiable information and seeding data accordingly. It integrates seamlessly into development workflows, providing data where it's needed most: on local machines, for CI/CD testing, and preview environments.

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.

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

Syntho is a self-service AI-generated synthetic data platform that offers a comprehensive solution for generating synthetic data for various purposes. It provides tools for de-identification, test data management, rule-based synthetic data generation, data masking, and more. With a focus on privacy and accuracy, Syntho enables users to create synthetic data that mirrors real production data while ensuring compliance with regulations and data privacy standards. The platform offers a range of features and use cases tailored to different industries, including healthcare, finance, and public organizations.

LambdaTest is a next-generation mobile apps and cross-browser testing cloud platform that offers a wide range of testing services. It allows users to perform manual live-interactive cross-browser testing, run Selenium, Cypress, Playwright scripts on cloud-based infrastructure, and execute AI-powered automation testing. The platform also provides accessibility testing, real devices cloud, visual regression cloud, and AI-powered test analytics. LambdaTest is trusted by over 2 million users globally and offers a unified digital experience testing cloud to accelerate go-to-market strategies.

BlazeMeter by Perforce is an AI-powered continuous testing platform designed to automate testing processes and enhance software quality. It offers effortless test creation, seamless test execution, instant issue analysis, and self-sustaining maintenance. BlazeMeter provides a comprehensive solution for performance, functional, scriptless, API testing, and monitoring, along with test data and service virtualization. The platform enables teams to speed up digital transformation, shift quality left, and streamline DevOps practices. With AI analytics, scriptless test creation, and UX testing capabilities, BlazeMeter empowers users to drive innovation, accuracy, and speed in their test automation efforts.

Byterat is a cloud-based platform that provides battery data management, visualization, and analytics. It offers an end-to-end data pipeline that automatically synchronizes, processes, and visualizes materials, manufacturing, and test data from all labs. Byterat also provides 24/7 access to experiments from anywhere in the world and integrates seamlessly with current workflows. It is customizable to specific cell chemistries and allows users to build custom visualizations, dashboards, and analyses. Byterat's AI-powered battery research has been published in leading journals, and its team has pioneered a new class of models that extract tell-tale signals of battery health from electrical signals to forecast future performance.

SearchCandy is a Generative AI-powered search and analytics tool designed for e-commerce businesses. It helps businesses understand their customers better, boost conversions, and gain valuable insights. The platform offers easy integration with popular data integrations like Azure, Google Cloud, and AWS, as well as API clients such as Node.js, Python, and .NET. With SearchCandy, businesses can connect their data, test data retrieval using conversational prompts, easily integrate into their e-commerce store, track customer conversions, and benefit from affordable pricing.

jsonAI is an AI tool that allows users to easily transform data into structured JSON format. Users can define their schema, add custom prompts, and receive AI-structured JSON responses. The tool enables users to create complex schemas with nested objects, control the response JSON on the fly, and test their JSON data in real-time. jsonAI offers a free trial plan, seamless integration with existing apps, and ensures data security by not storing user data on their servers.

The AI-powered Cambridge English Test platform offers exercises for English levels B1, B2, C1, and C2. Users can select exercise types such as Reading and Use of English, including activities like Open Cloze, Multiple Choice, Word Formation, and more. The AI, developed by Shining Apps in partnership with Use of English PRO, provides a unique learning experience by generating exercises from a database of over 5000 official exams. It uses advanced Natural Language Processing (NLP) to understand context, tweak exercises, and offer detailed feedback for effective learning.

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.

BugFree.ai is an AI-powered platform designed to help users practice system design and behavior interviews, similar to Leetcode. The platform offers a range of features to assist users in preparing for technical interviews, including mock interviews, real-time feedback, and personalized study plans. With BugFree.ai, users can improve their problem-solving skills and gain confidence in tackling complex interview questions.

Mobility Engineering is a website that provides news, articles, and resources on the latest developments in mobility technology. The site covers a wide range of topics, including autonomous vehicles, connected cars, electric vehicles, and more. Mobility Engineering is a valuable resource for anyone interested in staying up-to-date on the latest trends in mobility technology.

Contentable.ai is a platform for comparing multiple AI models, rapidly moving from prototyping to production, and management of your custom AI solutions across multiple vendors. It allows users to test multiple AI models in seconds, compare models side-by-side across top AI providers, collaborate on AI models with their team seamlessly, design complex AI workflows without coding, and pay as they go.

Eden AI is a full-stack AI platform designed for developers to efficiently create, test, and deploy AI solutions. It offers unified access to a wide range of AI models, a powerful workflow builder, and advanced monitoring tools. The platform enables users to integrate AI seamlessly into their projects, with features such as template-based workflow creation, AI model selection, cost monitoring, easy integration via API, and access to top AI technologies and cloud services.

Ottic is an AI tool designed to empower both technical and non-technical teams to test Language Model (LLM) applications efficiently and accelerate the development cycle. It offers features such as a 360º view of the QA process, end-to-end test management, comprehensive LLM evaluation, and real-time monitoring of user behavior. Ottic aims to bridge the gap between technical and non-technical team members, ensuring seamless collaboration and reliable product delivery.

Plumb is a no-code, node-based builder that empowers product, design, and engineering teams to create AI features together. It enables users to build, test, and deploy AI features with confidence, fostering collaboration across different disciplines. With Plumb, teams can ship prototypes directly to production, ensuring that the best prompts from the playground are the exact versions that go to production. It goes beyond automation, allowing users to build complex multi-tenant pipelines, transform data, and leverage validated JSON schema to create reliable, high-quality AI features that deliver real value to users. Plumb also makes it easy to compare prompt and model performance, enabling users to spot degradations, debug them, and ship fixes quickly. It is designed for SaaS teams, helping ambitious product teams collaborate to deliver state-of-the-art AI-powered experiences to their users at scale.

Face Symmetry Test is an AI-powered tool that analyzes the symmetry of facial features by detecting key landmarks such as eyes, nose, mouth, and chin. Users can upload a photo to receive a personalized symmetry score, providing insights into the balance and proportion of their facial features. The tool uses advanced AI algorithms to ensure accurate results and offers guidelines for improving the accuracy of the analysis. Face Symmetry Test is free to use and prioritizes user privacy and security by securely processing uploaded photos without storing or sharing data with third parties.

Lisapet.AI is an AI prompt testing suite designed for product teams to streamline the process of designing, prototyping, testing, and shipping AI features. It offers a comprehensive platform with features like best-in-class AI playground, variables for dynamic data inputs, structured outputs, side-by-side editing, function calling, image inputs, assertions & metrics, performance comparison, data sets organization, shareable reports, comments & feedback, token & cost stats, and more. The application aims to help teams save time, improve efficiency, and ensure the reliability of AI features through automated prompt testing.

Empirical is a tool that allows you to test different LLMs, prompts, and other model configurations across all the scenarios that matter for your application. With Empirical, you can run your test datasets locally against off-the-shelf models, test your own custom models and RAG applications, view, compare, and analyze outputs on a web UI, score your outputs with scoring functions, and run tests on CI/CD.

QGate-Model is a machine learning meta-model with synthetic data, designed for MLOps and feature store. It is independent of machine learning solutions, with definitions in JSON and data in CSV/parquet formats. This meta-model is useful for comparing capabilities and functions of machine learning solutions, independently testing new versions of machine learning solutions, and conducting various types of tests (unit, sanity, smoke, system, regression, function, acceptance, performance, shadow, etc.). It can also be used for external test coverage when internal test coverage is not available or weak.

A Python package that helps Data and Analytics engineers render dbt projects in Apache Airflow DAGs. It enables teams to automatically render their dbt projects in a granular level, creating individual Airflow tasks for every model, seed, snapshot, and test within the dbt project. This allows for full control at the task-level, improving visibility and management of data models within the team.

EvoMaster is an open-source AI-driven tool that automatically generates system-level test cases for web/enterprise applications. It uses an Evolutionary Algorithm and Dynamic Program Analysis to evolve test cases, maximizing code coverage and fault detection. The tool supports REST, GraphQL, and RPC APIs, with whitebox testing for JVM-compiled languages. It generates JUnit tests, detects faults, handles SQL databases, and supports authentication. EvoMaster has been funded by the European Research Council and the Research Council of Norway.

QANTA is a system and dataset for question answering tasks. It provides a script to download datasets, preprocesses questions, and matches them with Wikipedia pages. The system includes various datasets, training, dev, and test data in JSON and SQLite formats. Dependencies include Python 3.6, `click`, and NLTK models. Elastic Search 5.6 is needed for the Guesser component. Configuration is managed through environment variables and YAML files. QANTA supports multiple guesser implementations that can be enabled/disabled. Running QANTA involves using `cli.py` and Luigi pipelines. The system accesses raw Wikipedia dumps for data processing. The QANTA ID numbering scheme categorizes datasets based on events and competitions.

VLM-R1 is a stable and generalizable R1-style Large Vision-Language Model proposed for Referring Expression Comprehension (REC) task. It compares R1 and SFT approaches, showing R1 model's steady improvement on out-of-domain test data. The project includes setup instructions, training steps for GRPO and SFT models, support for user data loading, and evaluation process. Acknowledgements to various open-source projects and resources are mentioned. The project aims to provide a reliable and versatile solution for vision-language tasks.

OntoGPT is a Python package for extracting structured information from text using large language models, instruction prompts, and ontology-based grounding. It provides a command line interface and a minimal web app for easy usage. The tool has been evaluated on test data and is used in related projects like TALISMAN for gene set analysis. OntoGPT enables users to extract information from text by specifying relevant terms and provides the extracted objects as output.

AI Audio Datasets List (AI-ADL) is a comprehensive collection of datasets consisting of speech, music, and sound effects, used for Generative AI, AIGC, AI model training, and audio applications. It includes datasets for speech recognition, speech synthesis, music information retrieval, music generation, audio processing, sound synthesis, and more. The repository provides a curated list of diverse datasets suitable for various AI audio tasks.

SuperAdapters is a tool designed to finetune Large Language Models (LLMs) with various adapters on different platforms. It supports models like Bloom, LLaMA, ChatGLM, Qwen, Baichuan, Mixtral, Phi, and more. Users can finetune LLMs on Windows, Linux, and Mac M1/2, handle train/test data with Terminal, File, or DataBase, and perform tasks like CausalLM and SequenceClassification. The tool provides detailed instructions on how to use different models with specific adapters for tasks like finetuning and inference. It also includes requirements for CentOS, Ubuntu, and MacOS, along with information on LLM downloads and data formats. Additionally, it offers parameters for finetuning and inference, as well as options for web and API-based inference.

Recommenders is a project under the Linux Foundation of AI and Data that assists researchers, developers, and enthusiasts in prototyping, experimenting with, and bringing to production a range of classic and state-of-the-art recommendation systems. The repository contains examples and best practices for building recommendation systems, provided as Jupyter notebooks. It covers tasks such as preparing data, building models using various recommendation algorithms, evaluating algorithms, tuning hyperparameters, and operationalizing models in a production environment on Azure. The project provides utilities to support common tasks like loading datasets, evaluating model outputs, and splitting training/test data. It includes implementations of state-of-the-art algorithms for self-study and customization in applications.

Chat with your data using OpenAI and AI Search. This solution accelerator uses an Azure OpenAI GPT model and an Azure AI Search index generated from your data, which is integrated into a web application to provide a natural language interface, including speech-to-text functionality, for search queries. Users can drag and drop files, point to storage, and take care of technical setup to transform documents. There is a web app that users can create in their own subscription with security and authentication.

The repository Adaptive-MT-LLM-Fine-tuning contains code and data for the paper 'Fine-tuning Large Language Models for Adaptive Machine Translation'. It focuses on enhancing Mistral 7B, a large language model, for real-time adaptive machine translation in the medical domain. The fine-tuning process involves using zero-shot and one-shot translation prompts to improve terminology and style adherence. The repository includes training and test data, data processing code, fuzzy match retrieval techniques, fine-tuning methods, conversion to CTranslate2 format, tokenizers, translation codes, and evaluation metrics.

Eval-Scope is a framework for evaluating and improving large language models (LLMs). It provides a set of commonly used test datasets, metrics, and a unified model interface for generating and evaluating LLM responses. Eval-Scope also includes an automatic evaluator that can score objective questions and use expert models to evaluate complex tasks. Additionally, it offers a visual report generator, an arena mode for comparing multiple models, and a variety of other features to support LLM evaluation and development.

pytest-evals is a minimalistic pytest plugin designed to help evaluate the performance of Language Model (LLM) outputs against test cases. It allows users to test and evaluate LLM prompts against multiple cases, track metrics, and integrate easily with pytest, Jupyter notebooks, and CI/CD pipelines. Users can scale up by running tests in parallel with pytest-xdist and asynchronously with pytest-asyncio. The tool focuses on simplifying evaluation processes without the need for complex frameworks, keeping tests and evaluations together, and emphasizing logic over infrastructure.

Deepchecks is a holistic open-source solution for AI & ML validation needs, enabling thorough testing of data and models from research to production. It includes components for testing, CI & testing management, and monitoring. Users can install and use Deepchecks for testing and monitoring their AI models, with customizable checks and suites for tabular, NLP, and computer vision data. The tool provides visual reports, pythonic/json output for processing, and a dynamic UI for collaboration and monitoring. Deepchecks is open source, with premium features available under a commercial license for monitoring components.

CompressAI-Vision is a tool that helps you develop, test, and evaluate compression models with standardized tests in the context of compression methods optimized for machine tasks algorithms such as Neural-Network (NN)-based detectors. It currently focuses on two types of pipeline: Video compression for remote inference (`compressai-remote-inference`), which corresponds to the MPEG "Video Coding for Machines" (VCM) activity. Split inference (`compressai-split-inference`), which includes an evaluation framework for compressing intermediate features produced in the context of split models. The software supports all the pipelines considered in the related MPEG activity: "Feature Compression for Machines" (FCM).

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.

SciCode is a challenging benchmark designed to evaluate the capabilities of language models (LMs) in generating code for solving realistic scientific research problems. It contains 338 subproblems decomposed from 80 challenging main problems across 16 subdomains from 6 domains. The benchmark offers optional descriptions specifying useful scientific background information and scientist-annotated gold-standard solutions and test cases for evaluation. SciCode demonstrates a realistic workflow of identifying critical science concepts and facts and transforming them into computation and simulation code, aiming to help showcase LLMs' progress towards assisting scientists and contribute to the future building and evaluation of scientific AI.

Crafting full, accurate Apex test classes, with 100% user service.

Makes every Sofware Tester an Expert Prompt Engineer

Expert in advanced data analysis and interpretation

Calculate the results of your A/B test and check whether the result is statistically significant or due to chance.

ABテストのサポート専門家

Provides Raven's Progressive Matrices test with explanations and calculates your IQ score.

Formal and structured interactive IQ test platform.

IQ Test is designed to simulate an IQ testing environment. It provides a formal and objective experience, delivering questions and processing answers in a straightforward manner.

I conduct a Turing test and try to decide if you are an AI

Test ideas for cognitive biases or challenge your own views. Member of the Hipster Energy Team. https://hipster.energy/team

Specializes in creating, refining, and testing data analysis prompts based on user queries.

児童、生徒、学生のテストの自己採点物を分析し、文化や私生活を考慮した学習のアドバイスを行います。(This program analyzes the self-graded test items of children, students, and students, and advises them on their studies, taking into account their cultural and personal lives.)

Python code annotator and test generator

For "Generative AI Test". Examiner in Generative AI, posing questions and providing feedback.

Sequentially administers Big Five Test with 1-5 scale.

The main objective of a personalized ChatGPT for reading laboratory tests is to evaluate laboratory test results and create a spreadsheet with the evaluation results and possible solutions.

The Turing Test, first named the imitation game by Alan Turing in 1950, is a measure of a machine's capacity to demonstrate intelligence that's either equal to or indistinguishable from human intelligence.

Checks through Embedded System Datasheet and Suggest Source Codes

Get 1-on-1 tutoring. Trained from official questions only (verbal, quant, data insights). Score in the 90th percentile! 🚀

Enhanced with biomedical document knowledge for in-depth blood test analysis.