Best AI tools for< Evaluate Learning >

Arize AI is an AI Observability & LLM Evaluation Platform that helps you monitor, troubleshoot, and evaluate your machine learning models. With Arize, you can catch model issues, troubleshoot root causes, and continuously improve performance. Arize is used by top AI companies to surface, resolve, and improve their models.

BenchLLM is an AI tool designed for AI engineers to evaluate LLM-powered apps by running and evaluating models with a powerful CLI. It allows users to build test suites, choose evaluation strategies, and generate quality reports. The tool supports OpenAI, Langchain, and other APIs out of the box, offering automation, visualization of reports, and monitoring of model performance.

Langtrace AI is an open-source observability tool powered by Scale3 Labs that helps monitor, evaluate, and improve LLM (Large Language Model) applications. It collects and analyzes traces and metrics to provide insights into the ML pipeline, ensuring security through SOC 2 Type II certification. Langtrace supports popular LLMs, frameworks, and vector databases, offering end-to-end observability and the ability to build and deploy AI applications with confidence.

thisorthis.ai is an AI tool that allows users to compare generative AI models and AI model responses. It helps users analyze and evaluate different AI models to make informed decisions. The tool requires JavaScript to be enabled for optimal functionality.

HappyML is an AI tool designed to assist users in machine learning tasks. It provides a user-friendly interface for running machine learning algorithms without the need for complex coding. With HappyML, users can easily build, train, and deploy machine learning models for various applications. The tool offers a range of features such as data preprocessing, model evaluation, hyperparameter tuning, and model deployment. HappyML simplifies the machine learning process, making it accessible to users with varying levels of expertise.

ClassPoint is an AI quiz generator tool integrated with PowerPoint that allows users to effortlessly create engaging quiz questions from their presentation slides. The tool leverages AI technology to analyze slide content and generate various quiz types, such as Multiple Choice, Short Answer, and Fill in the Blanks. With multi-language support powered by OpenAI, ClassPoint caters to diverse user needs, enabling educators to captivate students and evaluate their understanding through interactive quizzes. The tool also enhances questioning strategies by incorporating Bloom's Taxonomy levels, fostering critical thinking and creativity in quizzes. ClassPoint's AI feature simplifies quiz creation, live response collection, and grading, making teaching more interactive and efficient.

Mangus is an AI-powered learning platform that provides personalized learning paths for employees and students. It offers a wide range of courses and programs in various disciplines, including business, education, technology, and more. Mangus uses gamification and artificial intelligence to create an engaging and effective learning experience.

edu720 is a science-backed learning platform that uses AI and nanolearning to redefine how workforces learn and achieve their goals. It provides pre-built learning modules on various topics, including cybersecurity, privacy, and AI ethics. edu720's 360-degree approach ensures that all employees, regardless of their status or location, fully understand and absorb the knowledge conveyed.

Evidently AI is an open-source machine learning (ML) monitoring and observability platform that helps data scientists and ML engineers evaluate, test, and monitor ML models from validation to production. It provides a centralized hub for ML in production, including data quality monitoring, data drift monitoring, ML model performance monitoring, and NLP and LLM monitoring. Evidently AI's features include customizable reports, structured checks for data and models, and a Python library for ML monitoring. It is designed to be easy to use, with a simple setup process and a user-friendly interface. Evidently AI is used by over 2,500 data scientists and ML engineers worldwide, and it has been featured in publications such as Forbes, VentureBeat, and TechCrunch.

Datumbox is a machine learning platform that offers a powerful open-source Machine Learning Framework written in Java. It provides a large collection of algorithms, models, statistical tests, and tools to power up intelligent applications. The platform enables developers to build smart software and services quickly using its REST Machine Learning API. Datumbox API offers off-the-shelf Classifiers and Natural Language Processing services for applications like Sentiment Analysis, Topic Classification, Language Detection, and more. It simplifies the process of designing and training Machine Learning models, making it easy for developers to create innovative applications.

FinetuneDB is an AI fine-tuning platform that allows users to easily create and manage datasets to fine-tune LLMs, evaluate outputs, and iterate on production data. It integrates with open-source and proprietary foundation models, and provides a collaborative editor for building datasets. FinetuneDB also offers a variety of features for evaluating model performance, including human and AI feedback, automated evaluations, and model metrics tracking.

AI Tools Masters is a comprehensive platform that empowers users to discover and evaluate the latest and most exceptional AI tools. Catering to diverse needs, from education to personal advancement, AI Tools Masters offers a curated collection of top-notch solutions tailored to specific requirements. With a user-friendly interface and extensive filtering options, users can effortlessly navigate through a wide range of AI tools, ensuring they find the perfect fit for their projects and goals.

Entry Point AI is a modern AI optimization platform for fine-tuning proprietary and open-source language models. It provides a user-friendly interface to manage prompts, fine-tunes, and evaluations in one place. The platform enables users to optimize models from leading providers, train across providers, work collaboratively, write templates, import/export data, share models, and avoid common pitfalls associated with fine-tuning. Entry Point AI simplifies the fine-tuning process, making it accessible to users without the need for extensive data, infrastructure, or insider knowledge.

integrate.ai is a platform that enables data and analytics providers to collaborate easily with enterprise data science teams without moving data. Powered by federated learning technology, the platform allows for efficient proof of concepts, data experimentation, infrastructure agnostic evaluations, collaborative data evaluations, and data governance controls. It supports various data science jobs such as match rate analysis, exploratory data analysis, correlation analysis, model performance analysis, feature importance & data influence, and model validation. The platform integrates with popular data science tools like Azure, Jupyter, Databricks, AWS, GCP, Snowflake, Pandas, PyTorch, MLflow, and scikit-learn.

SiMa.ai is an AI application that offers high-performance, power-efficient, and scalable edge machine learning solutions for various industries such as automotive, industrial, healthcare, drones, and government sectors. The platform provides MLSoC™ boards, DevKit 2.0, Palette Software 1.2, and Edgematic™ for developers to accelerate complete applications and deploy AI-enabled solutions. SiMa.ai's Machine Learning System on Chip (MLSoC) enables full-pipeline implementations of real-world ML solutions, making it a trusted platform for edge AI development.

Emerj is a leading provider of enterprise AI insights, research, and connections to the right AI tools and providers. We cover AI use-cases and impact in the world’s largest organizations. Our mission is to help businesses understand and implement AI to achieve their business goals.

Inductor is a developer tool for evaluating, ensuring, and improving the quality of your LLM applications – both during development and in production. It provides a fantastic workflow for continuous testing and evaluation as you develop, so that you always know your LLM app’s quality. Systematically improve quality and cost-effectiveness by actionably understanding your LLM app’s behavior and quickly testing different app variants. Rigorously assess your LLM app’s behavior before you deploy, in order to ensure quality and cost-effectiveness when you’re live. Easily monitor your live traffic: detect and resolve issues, analyze usage in order to improve, and seamlessly feed back into your development process. Inductor makes it easy for engineering and other roles to collaborate: get critical human feedback from non-engineering stakeholders (e.g., PM, UX, or subject matter experts) to ensure that your LLM app is user-ready.

Confident AI is an open-source evaluation infrastructure for Large Language Models (LLMs). It provides a centralized platform to judge LLM applications, ensuring substantial benefits and addressing any weaknesses in LLM implementation. With Confident AI, companies can define ground truths to ensure their LLM is behaving as expected, evaluate performance against expected outputs to pinpoint areas for iterations, and utilize advanced diff tracking to guide towards the optimal LLM stack. The platform offers comprehensive analytics to identify areas of focus and features such as A/B testing, evaluation, output classification, reporting dashboard, dataset generation, and detailed monitoring to help productionize LLMs with confidence.

Sacred is a tool to configure, organize, log and reproduce computational experiments. It is designed to introduce only minimal overhead, while encouraging modularity and configurability of experiments. The ability to conveniently make experiments configurable is at the heart of Sacred. If the parameters of an experiment are exposed in this way, it will help you to: keep track of all the parameters of your experiment easily run your experiment for different settings save configurations for individual runs in files or a database reproduce your results In Sacred we achieve this through the following main mechanisms: Config Scopes are functions with a @ex.config decorator, that turn all local variables into configuration entries. This helps to set up your configuration really easily. Those entries can then be used in captured functions via dependency injection. That way the system takes care of passing parameters around for you, which makes using your config values really easy. The command-line interface can be used to change the parameters, which makes it really easy to run your experiment with modified parameters. Observers log every information about your experiment and the configuration you used, and saves them for example to a Database. This helps to keep track of all your experiments. Automatic seeding helps controlling the randomness in your experiments, such that they stay reproducible.

Robust Intelligence is an end-to-end solution for securing AI applications. It automates the evaluation of AI models, data, and files for security and safety vulnerabilities and provides guardrails for AI applications in production against integrity, privacy, abuse, and availability violations. Robust Intelligence helps enterprises remove AI security blockers, save time and resources, meet AI safety and security standards, align AI security across stakeholders, and protect against evolving threats.

Adversarial Robustness Toolbox (ART) is a Python library for Machine Learning Security. ART provides tools that enable developers and researchers to defend and evaluate Machine Learning models and applications against the adversarial threats of Evasion, Poisoning, Extraction, and Inference. ART supports all popular machine learning frameworks (TensorFlow, Keras, PyTorch, MXNet, scikit-learn, XGBoost, LightGBM, CatBoost, GPy, etc.), all data types (images, tables, audio, video, etc.) and machine learning tasks (classification, object detection, speech recognition, generation, certification, etc.).

MONAI is a PyTorch-based, open-source framework for deep learning in healthcare imaging. It provides a comprehensive set of tools for medical image analysis, including data preprocessing, model training, and evaluation. MONAI is designed to be flexible and easy to use, making it a valuable resource for researchers and developers in the field of medical imaging.

The 100 Days in AI repository provides a comprehensive roadmap for individuals to learn Artificial Intelligence over a period of 100 days. It covers topics ranging from basic programming in Python to advanced concepts in AI, including machine learning, deep learning, and specialized AI topics. The repository includes daily tasks, resources, and exercises to ensure a structured learning experience. By following this roadmap, users can gain a solid understanding of AI and be prepared to work on real-world AI projects.

A public repository collecting links to state-of-the-art training sets, QA, benchmarks and other evaluations for various ML and LLM applications in materials science and chemistry. It includes datasets related to chemistry, materials, multimodal data, and knowledge graphs in the field. The repository aims to provide resources for training and evaluating machine learning models in the materials science and chemistry domains.

The repository 'Awesome-LLM4Cybersecurity' provides a comprehensive overview of the applications of Large Language Models (LLMs) in cybersecurity. It includes a systematic literature review covering topics such as constructing cybersecurity-oriented domain LLMs, potential applications of LLMs in cybersecurity, and research directions in the field. The repository analyzes various benchmarks, datasets, and applications of LLMs in cybersecurity tasks like threat intelligence, fuzzing, vulnerabilities detection, insecure code generation, program repair, anomaly detection, and LLM-assisted attacks.

An Open-Source Engineering Guide for Prompt-in-context-learning from EgoAlpha Lab. 📝 Papers | ⚡️ Playground | 🛠 Prompt Engineering | 🌍 ChatGPT Prompt | ⛳ LLMs Usage Guide > **⭐️ Shining ⭐️:** This is fresh, daily-updated resources for in-context learning and prompt engineering. As Artificial General Intelligence (AGI) is approaching, let’s take action and become a super learner so as to position ourselves at the forefront of this exciting era and strive for personal and professional greatness. The resources include: _🎉Papers🎉_: The latest papers about _In-Context Learning_ , _Prompt Engineering_ , _Agent_ , and _Foundation Models_. _🎉Playground🎉_: Large language models（LLMs）that enable prompt experimentation. _🎉Prompt Engineering🎉_: Prompt techniques for leveraging large language models. _🎉ChatGPT Prompt🎉_: Prompt examples that can be applied in our work and daily lives. _🎉LLMs Usage Guide🎉_: The method for quickly getting started with large language models by using LangChain. In the future, there will likely be two types of people on Earth (perhaps even on Mars, but that's a question for Musk): - Those who enhance their abilities through the use of AIGC; - Those whose jobs are replaced by AI automation. 💎EgoAlpha: Hello! human👤, are you ready?

ML-Bench is a tool designed to evaluate large language models and agents for machine learning tasks on repository-level code. It provides functionalities for data preparation, environment setup, usage, API calling, open source model fine-tuning, and inference. Users can clone the repository, load datasets, run ML-LLM-Bench, prepare data, fine-tune models, and perform inference tasks. The tool aims to facilitate the evaluation of language models and agents in the context of machine learning tasks on code repositories.

LLM-RGB is a repository containing a collection of detailed test cases designed to evaluate the reasoning and generation capabilities of Language Learning Models (LLMs) in complex scenarios. The benchmark assesses LLMs' performance in understanding context, complying with instructions, and handling challenges like long context lengths, multi-step reasoning, and specific response formats. Each test case evaluates an LLM's output based on context length difficulty, reasoning depth difficulty, and instruction compliance difficulty, with a final score calculated for each test case. The repository provides a score table, evaluation details, and quick start guide for running evaluations using promptfoo testing tools.

A repository sharing the panorama of the methodology literature on Transformer architecture upgrades in Large Language Models for handling extensive context windows, with real-time updating the newest published works. It includes a survey on advancing Transformer architecture in long-context large language models, flash-ReRoPE implementation, latest news on data engineering, lightning attention, Kimi AI assistant, chatglm-6b-128k, gpt-4-turbo-preview, benchmarks like InfiniteBench and LongBench, long-LLMs-evals for evaluating methods for enhancing long-context capabilities, and LLMs-learning for learning technologies and applicated tasks about Large Language Models.

StableToolBench is a new benchmark developed to address the instability of Tool Learning benchmarks. It aims to balance stability and reality by introducing features such as a Virtual API System with caching and API simulators, a new set of solvable queries determined by LLMs, and a Stable Evaluation System using GPT-4. The Virtual API Server can be set up either by building from source or using a prebuilt Docker image. Users can test the server using provided scripts and evaluate models with Solvable Pass Rate and Solvable Win Rate metrics. The tool also includes model experiments results comparing different models' performance.

Raga LLM Hub is a comprehensive evaluation toolkit for Language and Learning Models (LLMs) with over 100 meticulously designed metrics. It allows developers and organizations to evaluate and compare LLMs effectively, establishing guardrails for LLMs and Retrieval Augmented Generation (RAG) applications. The platform assesses aspects like Relevance & Understanding, Content Quality, Hallucination, Safety & Bias, Context Relevance, Guardrails, and Vulnerability scanning, along with Metric-Based Tests for quantitative analysis. It helps teams identify and fix issues throughout the LLM lifecycle, revolutionizing reliability and trustworthiness.

LLMOps Workshop is a course designed to help users build, evaluate, monitor, and deploy Large Language Model solutions efficiently using Azure AI, Azure Machine Learning Prompt Flow, Content Safety, and Azure OpenAI. The workshop covers various aspects of LLMOps to help users master the process.

**deep** doctection is a Python library that orchestrates document extraction and document layout analysis tasks using deep learning models. It does not implement models but enables you to build pipelines using highly acknowledged libraries for object detection, OCR and selected NLP tasks and provides an integrated framework for fine-tuning, evaluating and running models. For more specific text processing tasks use one of the many other great NLP libraries. **deep** doctection focuses on applications and is made for those who want to solve real world problems related to document extraction from PDFs or scans in various image formats. **deep** doctection provides model wrappers of supported libraries for various tasks to be integrated into pipelines. Its core function does not depend on any specific deep learning library. Selected models for the following tasks are currently supported: * Document layout analysis including table recognition in Tensorflow with **Tensorpack**, or PyTorch with **Detectron2**, * OCR with support of **Tesseract**, **DocTr** (Tensorflow and PyTorch implementations available) and a wrapper to an API for a commercial solution, * Text mining for native PDFs with **pdfplumber**, * Language detection with **fastText**, * Deskewing and rotating images with **jdeskew**. * Document and token classification with all LayoutLM models provided by the **Transformer library**. (Yes, you can use any LayoutLM-model with any of the provided OCR-or pdfplumber tools straight away!). * Table detection and table structure recognition with **table-transformer**. * There is a small dataset for token classification available and a lot of new tutorials to show, how to train and evaluate this dataset using LayoutLMv1, LayoutLMv2, LayoutXLM and LayoutLMv3. * Comprehensive configuration of **analyzer** like choosing different models, output parsing, OCR selection. Check this notebook or the docs for more infos. * Document layout analysis and table recognition now runs with **Torchscript** (CPU) as well and **Detectron2** is not required anymore for basic inference. * [**new**] More angle predictors for determining the rotation of a document based on **Tesseract** and **DocTr** (not contained in the built-in Analyzer). * [**new**] Token classification with **LiLT** via **transformers**. We have added a model wrapper for token classification with LiLT and added a some LiLT models to the model catalog that seem to look promising, especially if you want to train a model on non-english data. The training script for LayoutLM can be used for LiLT as well and we will be providing a notebook on how to train a model on a custom dataset soon. **deep** doctection provides on top of that methods for pre-processing inputs to models like cropping or resizing and to post-process results, like validating duplicate outputs, relating words to detected layout segments or ordering words into contiguous text. You will get an output in JSON format that you can customize even further by yourself. Have a look at the **introduction notebook** in the notebook repo for an easy start. Check the **release notes** for recent updates. **deep** doctection or its support libraries provide pre-trained models that are in most of the cases available at the **Hugging Face Model Hub** or that will be automatically downloaded once requested. For instance, you can find pre-trained object detection models from the Tensorpack or Detectron2 framework for coarse layout analysis, table cell detection and table recognition. Training is a substantial part to get pipelines ready on some specific domain, let it be document layout analysis, document classification or NER. **deep** doctection provides training scripts for models that are based on trainers developed from the library that hosts the model code. Moreover, **deep** doctection hosts code to some well established datasets like **Publaynet** that makes it easy to experiment. It also contains mappings from widely used data formats like COCO and it has a dataset framework (akin to **datasets** so that setting up training on a custom dataset becomes very easy. **This notebook** shows you how to do this. **deep** doctection comes equipped with a framework that allows you to evaluate predictions of a single or multiple models in a pipeline against some ground truth. Check again **here** how it is done. Having set up a pipeline it takes you a few lines of code to instantiate the pipeline and after a for loop all pages will be processed through the pipeline.

LLM Foundry is a codebase for training, finetuning, evaluating, and deploying LLMs for inference with Composer and the MosaicML platform. It is designed to be easy-to-use, efficient _and_ flexible, enabling rapid experimentation with the latest techniques. You'll find in this repo: * `llmfoundry/` - source code for models, datasets, callbacks, utilities, etc. * `scripts/` - scripts to run LLM workloads * `data_prep/` - convert text data from original sources to StreamingDataset format * `train/` - train or finetune HuggingFace and MPT models from 125M - 70B parameters * `train/benchmarking` - profile training throughput and MFU * `inference/` - convert models to HuggingFace or ONNX format, and generate responses * `inference/benchmarking` - profile inference latency and throughput * `eval/` - evaluate LLMs on academic (or custom) in-context-learning tasks * `mcli/` - launch any of these workloads using MCLI and the MosaicML platform * `TUTORIAL.md` - a deeper dive into the repo, example workflows, and FAQs

FiftyOne is an open-source tool designed for building high-quality datasets and computer vision models. It supercharges machine learning workflows by enabling users to visualize datasets, interpret models faster, and improve efficiency. With FiftyOne, users can explore scenarios, identify failure modes, visualize complex labels, evaluate models, find annotation mistakes, and much more. The tool aims to streamline the process of improving machine learning models by providing a comprehensive set of features for data analysis and model interpretation.

TruLens provides a set of tools for developing and monitoring neural nets, including large language models. This includes both tools for evaluation of LLMs and LLM-based applications with _TruLens-Eval_ and deep learning explainability with _TruLens-Explain_. _TruLens-Eval_ and _TruLens-Explain_ are housed in separate packages and can be used independently.

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.

StableToolBench is a new benchmark developed to address the instability of Tool Learning benchmarks. It aims to balance stability and reality by introducing features like Virtual API System, Solvable Queries, and Stable Evaluation System. The benchmark ensures consistency through a caching system and API simulators, filters queries based on solvability using LLMs, and evaluates model performance using GPT-4 with metrics like Solvable Pass Rate and Solvable Win Rate.

This repository provides an original implementation of Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications. It includes tools for neuron-level pruning, pruning based on set difference, Wanda/SNIP score dumping, rank-level pruning, and rank removal with orthogonal projection. Users can specify parameters like prune method, datasets, sparsity ratio, model, and save location to evaluate and modify neural networks for safety alignment.

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I provide a structured way of using AI to support teaching and learning. I use the the CHOICE method (i.e., Clarify, Harness, Originate, Iterate, Communicate, Evaluate) to ensure that your use of AI can help you meet your educational goals.

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