Best AI tools for< Test Data >

Zelma is an AI-powered research assistant that enables users to find, graph, and understand U.S. school testing data using plain English queries. It allows users to search student test data by school district, demographics, grade, and more, and presents the results with graphs, tables, and descriptions. Zelma aims to make education data accessible and understandable for everyone.

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.

Syntho is a self-service AI-generated Synthetic Data Platform that offers a comprehensive solution for generating synthetic data with high accuracy and privacy. It provides tools for de-identification, test data management, rule-based synthetic data generation, and more. Syntho aims to simplify data collaborations, streamline data sharing, and accelerate innovation by leveraging AI technology to create secure and nonsensitive synthetic data for various industries.

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.

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.

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.

Keploy is an AI tool designed for developers to generate API tests efficiently. It is an open-source platform that converts API calls to test cases with data mocks. Keploy simplifies testing by capturing network interactions and generating automated tests, helping teams accelerate development with streamlined testing processes. The tool allows users to record and replay complex API flows, find duplicate tests, and seamlessly integrate with popular testing libraries like JUnit, PyTest, Jest, and Go-Test in CI/CD pipelines.

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.

Prelaunch.com is a product validation platform that helps creators test market demand before launching a product. It provides tools for concept validation, price validation, positioning testing, and idea validation. Users can gather early customer feedback, define pricing, identify target audiences, and make data-driven decisions to either launch confidently or pivot their product idea. The platform offers AI market research assistance, co-creation opportunities, and analytics to optimize product launches.

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.

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.

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.

ABtesting.ai is an AI-powered A/B testing software that helps businesses optimize their landing pages for conversions. It uses GPT-3 to generate automated text suggestions for headlines, copy, and call to actions, saving businesses time and effort. The software also automatically chooses the best combinations of elements to show to users, boosting conversion rates in the process. ABtesting.ai is easy to use and requires no manual work, making it a great option for businesses of all sizes.

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.

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.

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.

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.

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.

Labelbox is a data-centric AI platform for enterprises to develop, optimize, and use AI to solve problems and power new products and services. Enterprises use Labelbox to curate data, generate high-quality human feedback data for computer vision and LLMs, evaluate model performance, and automate tasks by combining AI and human-centric workflows. The academic & research community uses Labelbox for cutting-edge AI research.

Polaris establishes a novel, industry‑certified standard to foster the development of impactful methods in AI-based drug discovery. This library is a Python client to interact with the Polaris Hub. It allows you to download Polaris datasets and benchmarks, evaluate a custom method against a Polaris benchmark, and create and upload new datasets and benchmarks.

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.

Unitxt is a customizable library for textual data preparation and evaluation tailored to generative language models. It natively integrates with common libraries like HuggingFace and LM-eval-harness and deconstructs processing flows into modular components, enabling easy customization and sharing between practitioners. These components encompass model-specific formats, task prompts, and many other comprehensive dataset processing definitions. The Unitxt-Catalog centralizes these components, fostering collaboration and exploration in modern textual data workflows. Beyond being a tool, Unitxt is a community-driven platform, empowering users to build, share, and advance their pipelines collaboratively.

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.

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.