Best AI tools for< Run Ml Experiments >

Comet ML is an extensible, fully customizable machine learning platform that aims to move ML forward by supporting productivity, reproducibility, and collaboration. It integrates with existing infrastructure and tools to manage, visualize, and optimize models from training runs to production monitoring. Users can track and compare training runs, create a model registry, and monitor models in production all in one platform. Comet's platform can be run on any infrastructure, enabling users to reshape their ML workflow and bring their existing software and data stack.

Functionize is an AI-powered test automation platform that helps enterprises improve their product quality and release faster. It uses machine learning to automate test creation, maintenance, and execution, and provides a range of features to help teams collaborate and manage their testing process. Functionize integrates with popular CI/CD tools and DevOps pipelines, and offers a range of pricing options to suit different needs.

UbiOps is an AI infrastructure platform that helps teams quickly run their AI & ML workloads as reliable and secure microservices. It offers powerful AI model serving and orchestration with unmatched simplicity, speed, and scale. UbiOps allows users to deploy models and functions in minutes, manage AI workloads from a single control plane, integrate easily with tools like PyTorch and TensorFlow, and ensure security and compliance by design. The platform supports hybrid and multi-cloud workload orchestration, rapid adaptive scaling, and modular applications with unique workflow management system.

Mystic.ai is an AI tool designed to deploy and scale Machine Learning models with ease. It offers a fully managed Kubernetes platform that runs in your own cloud, allowing users to deploy ML models in their own Azure/AWS/GCP account or in a shared GPU cluster. Mystic.ai provides cost optimizations, fast inference, simpler developer experience, and performance optimizations to ensure high-performance AI model serving. With features like pay-as-you-go API, cloud integration with AWS/Azure/GCP, and a beautiful dashboard, Mystic.ai simplifies the deployment and management of ML models for data scientists and AI engineers.

Modal is a high-performance cloud platform designed for developers, AI data, and ML teams. It offers a serverless environment for running generative AI models, large-scale batch jobs, job queues, and more. With Modal, users can bring their own code and leverage the platform's optimized container file system for fast cold boots and seamless autoscaling. The platform is engineered for large-scale workloads, allowing users to scale to hundreds of GPUs, pay only for what they use, and deploy functions to the cloud in seconds without the need for YAML or Dockerfiles. Modal also provides features for job scheduling, web endpoints, observability, and security compliance.

Radicalbit is an MLOps and AI Observability platform that helps businesses deploy, serve, observe, and explain their AI models. It provides a range of features to help data teams maintain full control over the entire data lifecycle, including real-time data exploration, outlier and drift detection, and model monitoring in production. Radicalbit can be seamlessly integrated into any ML stack, whether SaaS or on-prem, and can be used to run AI applications in minutes.

The Run Recommender is a web-based tool that helps runners find the perfect pair of running shoes. It uses a smart algorithm to suggest options based on your input, giving you a starting point in your search for the perfect pair. The Run Recommender is designed to be user-friendly and easy to use. Simply input your shoe width, age, weight, and other details, and the Run Recommender will generate a list of potential shoes that might suit your running style and body. You can also provide information about your running experience, distance, and frequency, and the Run Recommender will use this information to further refine its suggestions. Once you have a list of potential shoes, you can click on each shoe to learn more about it, including its features, benefits, and price. You can also search for the shoe on Amazon to find the best deals.

Dora is a no-code 3D animated website design platform that allows users to create stunning 3D and animated visuals without writing a single line of code. With Dora, designers, freelancers, and creative professionals can focus on what they do best: designing. The platform is tailored for professionals who prioritize design aesthetics without wanting to dive deep into the backend. Dora offers a variety of features, including a drag-and-connect constraint layout system, advanced animation capabilities, and pixel-perfect usability. With Dora, users can create responsive 3D and animated websites that translate seamlessly across devices.

Learn Playwright is a comprehensive platform offering resources for learning end-to-end testing using the Playwright automation framework. It provides a blog with in-depth subjects about end-to-end testing, an 'Ask AI' feature for querying ChatGPT about Playwright, and a Dev Tools section that serves as an all-in-one toolbox for QA engineers. Additionally, users can explore QA job opportunities, access answered questions about Playwright, browse a Discord forum archive, watch tutorials and conference talks, utilize a browser extension for generating Playwright locators, and refer to a QA Wiki for definitions of common end-to-end testing terms.

Symphony is an AI-powered programming tool that allows users to write programs using natural language. It simplifies the coding process by enabling users to interact with the tool through spoken language, making it easier for both beginners and experienced programmers to create code. Symphony leverages advanced natural language processing algorithms to understand and interpret user commands, translating them into executable code. With Symphony, users can seamlessly communicate their programming ideas without the need to write complex code syntax, enhancing productivity and efficiency in software development.

aify is an AI-native application framework and runtime that allows users to build AI-native applications quickly and easily. With aify, users can create applications by simply writing a YAML file. The platform also offers a ready-to-use AI chatbot UI for seamless integration. Additionally, aify provides features such as Emoji express for searching emojis by semantics. The framework is open source under the MIT license, making it accessible to developers of all levels.

Lumora is an AI tool designed to help users efficiently manage, optimize, and test prompts for various AI platforms. It offers features such as prompt organization, enhancement, testing, and development. Lumora aims to improve prompt outcomes and streamline prompt management for teams, providing a user-friendly interface and a playground for experimentation. The tool also integrates with various AI models for text, image, and video generation, allowing users to optimize prompts for better results.

Dora is an AI-powered platform that enables users to create 3D animated websites without the need for coding. It caters to designers, freelancers, and creative professionals who seek to design visually captivating websites effortlessly. With Dora, users can craft mesmerizing 3D and animated visuals that are responsive and seamlessly translate across devices. The platform is designed for professionals who prioritize design aesthetics and offers a no-code experience for those transitioning from other design tools. Dora leverages advanced AI algorithms to generate, customize, and deploy stunning landing pages, revolutionizing the web design process.

Magnet is an AI coding assistant that helps product teams fix issues, share AI threads, and organize projects. It integrates with Linear, GitHub, and Notion, and provides auto-suggested files and code files for personalized and accurate AI recommendations. Magnet also offers prompt templates to help users get started and suggests quick fixes for bugs or enhancements.

Devath is the world's first AI-powered SmartHome platform that revolutionizes the way users interact with their smart devices. It eliminates the need for writing extensive lines of code by allowing users to simply give instructions to the AI for seamless device control. With features like splash resistance and responsive design, Devath offers a user-friendly experience for managing smart home functionalities. The platform also enables developers to preview and test their apps before submission, providing a 99% faster publishing process. Devath is continuously evolving with user feedback and aims to enhance the SmartHome experience through AI copilots and customizable features. With Devath, users can control their devices from the web and enjoy free unlimited access to the AI era of SmartHome.

One-Commerce is an AI-powered application designed to streamline the e-commerce listing process. It utilizes gemini technology to automatically generate detailed product descriptions, specifications, and SEO keywords from a single image. With its innovative approach, One-Commerce aims to simplify and enhance the online selling experience for e-commerce businesses.

Replicate is an AI tool that allows users to run and fine-tune open-source models, deploy custom models at scale, and generate various types of content such as images, text, music, and speech with just one line of code. It offers a platform where users can access a wide range of AI models contributed by the community, fine-tune models with their own data, and deploy custom models using Cog, an open-source tool for packaging machine learning models.

Sessions is a cloud-based video conferencing and webinar platform that offers a range of features to help businesses run successful online meetings and events. With Sessions, users can create interactive agendas, share screens, record meetings, and host webinars with up to 1000 participants. Sessions also integrates with a variety of third-party tools, including Google Drive, Dropbox, and Slack, making it easy to collaborate with colleagues and share files. Additionally, Sessions offers a number of AI-powered features, such as automatic transcription and translation, to help users get the most out of their meetings.

CALA is a leading fashion platform that unifies design, development, production, and logistics into a single, digital platform. It provides tools and support to automate and optimize the supply chain from start to finish. CALA also offers a network of designers and suppliers, as well as AI-powered design tools to help generate moodboards, fresh ideas, and more.

Effy AI is a free performance management software for teams. It is AI-powered and backed by Run your first 360 review in 60 sec. Fast, and stress-free 360 feedback and performance review software build for teams. With Effy AI, you can collect reviews from different sources such as self, peer, manager, and subordinate evaluations. The platform goes even further by allowing employees to suggest particular peers and seek approval from their manager, giving them a voice in their reviews. Effy AI uses cutting-edge artificial intelligence to carefully process reviewers' answers and generate comprehensive reports for each employee based on the review responses.

MetricsMLNotebooks is a repository containing applied causal ML notebooks. It provides a collection of notebooks for users to explore and run causal machine learning models. The repository includes both Python and R notebooks, with a focus on generating .Rmd files through a Github Action. Users can easily install the required packages by running 'pip install -r requirements.txt'. Note that any changes to .Rmd files will be overwritten by the corresponding .irnb files during the Github Action process. Additionally, all notebooks and R Markdown files are stripped from their outputs when pushed to the main branch, so users are advised to strip the notebooks before pushing to the repository.

OptScale is an open-source FinOps and MLOps platform that provides cloud cost optimization for all types of organizations and MLOps capabilities like experiment tracking, model versioning, ML leaderboards.

MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). MLflow's current components are: * `MLflow Tracking `_: An API to log parameters, code, and results in machine learning experiments and compare them using an interactive UI. * `MLflow Projects `_: A code packaging format for reproducible runs using Conda and Docker, so you can share your ML code with others. * `MLflow Models `_: A model packaging format and tools that let you easily deploy the same model (from any ML library) to batch and real-time scoring on platforms such as Docker, Apache Spark, Azure ML and AWS SageMaker. * `MLflow Model Registry `_: A centralized model store, set of APIs, and UI, to collaboratively manage the full lifecycle of MLflow Models.

Collective Mind (CM) is a collection of portable, extensible, technology-agnostic and ready-to-use automation recipes with a human-friendly interface (aka CM scripts) to unify and automate all the manual steps required to compose, run, benchmark and optimize complex ML/AI applications on any platform with any software and hardware: see online catalog and source code. CM scripts require Python 3.7+ with minimal dependencies and are continuously extended by the community and MLCommons members to run natively on Ubuntu, MacOS, Windows, RHEL, Debian, Amazon Linux and any other operating system, in a cloud or inside automatically generated containers while keeping backward compatibility - please don't hesitate to report encountered issues here and contact us via public Discord Server to help this collaborative engineering effort! CM scripts were originally developed based on the following requirements from the MLCommons members to help them automatically compose and optimize complex MLPerf benchmarks, applications and systems across diverse and continuously changing models, data sets, software and hardware from Nvidia, Intel, AMD, Google, Qualcomm, Amazon and other vendors: * must work out of the box with the default options and without the need to edit some paths, environment variables and configuration files; * must be non-intrusive, easy to debug and must reuse existing user scripts and automation tools (such as cmake, make, ML workflows, python poetry and containers) rather than substituting them; * must have a very simple and human-friendly command line with a Python API and minimal dependencies; * must require minimal or zero learning curve by using plain Python, native scripts, environment variables and simple JSON/YAML descriptions instead of inventing new workflow languages; * must have the same interface to run all automations natively, in a cloud or inside containers. CM scripts were successfully validated by MLCommons to modularize MLPerf inference benchmarks and help the community automate more than 95% of all performance and power submissions in the v3.1 round across more than 120 system configurations (models, frameworks, hardware) while reducing development and maintenance costs.

Windows Agent Arena (WAA) is a scalable Windows AI agent platform designed for testing and benchmarking multi-modal, desktop AI agents. It provides researchers and developers with a reproducible and realistic Windows OS environment for AI research, enabling testing of agentic AI workflows across various tasks. WAA supports deploying agents at scale using Azure ML cloud infrastructure, allowing parallel running of multiple agents and delivering quick benchmark results for hundreds of tasks in minutes.

ZenML is an extensible, open-source MLOps framework for creating portable, production-ready machine learning pipelines. By decoupling infrastructure from code, ZenML enables developers across your organization to collaborate more effectively as they develop to production.

Pax is a framework to configure and run machine learning experiments on top of Jax.

Neptune is a scalable experiment tracker for teams training foundation models. Log millions of runs, effortlessly monitor and visualize model training, and deploy on your infrastructure. Track 100% of metadata to accelerate AI breakthroughs. Log and display any framework and metadata type from any ML pipeline. Organize experiments with nested structures and custom dashboards. Compare results, visualize training, and optimize models quicker. Version models, review stages, and access production-ready models. Share results, manage users, and projects. Integrate with 25+ frameworks. Trusted by great companies to improve workflow.

The RAG Experiment Accelerator is a versatile tool that helps you conduct experiments and evaluations using Azure AI Search and RAG pattern. It offers a rich set of features, including experiment setup, integration with Azure AI Search, Azure Machine Learning, MLFlow, and Azure OpenAI, multiple document chunking strategies, query generation, multiple search types, sub-querying, re-ranking, metrics and evaluation, report generation, and multi-lingual support. The tool is designed to make it easier and faster to run experiments and evaluations of search queries and quality of response from OpenAI, and is useful for researchers, data scientists, and developers who want to test the performance of different search and OpenAI related hyperparameters, compare the effectiveness of various search strategies, fine-tune and optimize parameters, find the best combination of hyperparameters, and generate detailed reports and visualizations from experiment results.

The AI Scientist is a comprehensive system for fully automatic scientific discovery, enabling Foundation Models to perform research independently. It aims to tackle the grand challenge of developing agents capable of conducting scientific research and discovering new knowledge. The tool generates papers on various topics using Large Language Models (LLMs) and provides a platform for exploring new research ideas. Users can create their own templates for specific areas of study and run experiments to generate papers. However, caution is advised as the codebase executes LLM-written code, which may pose risks such as the use of potentially dangerous packages and web access.

ClearML Server is a backend service infrastructure for ClearML, facilitating collaboration and experiment management. It includes a web app, RESTful API, and file server for storing images and models. Users can deploy ClearML Server using Docker, AWS EC2 AMI, or Kubernetes. The system design supports single IP or sub-domain configurations with specific open ports. ClearML-Agent Services container allows launching long-lasting jobs and various use cases like auto-scaler service, controllers, optimizer, and applications. Advanced functionality includes web login authentication and non-responsive experiments watchdog. Upgrading ClearML Server involves stopping containers, backing up data, downloading the latest docker-compose.yml file, configuring ClearML-Agent Services, and spinning up docker containers. Community support is available through ClearML FAQ, Stack Overflow, GitHub issues, and email contact.

ClearML is a suite of tools designed to streamline the machine learning workflow. It includes an experiment manager, MLOps/LLMOps, data management, and model serving capabilities. ClearML is open-source and offers a free tier hosting option. It supports various ML/DL frameworks and integrates with Jupyter Notebook and PyCharm. ClearML provides extensive logging capabilities, including source control info, execution environment, hyper-parameters, and experiment outputs. It also offers automation features, such as remote job execution and pipeline creation. ClearML is designed to be easy to integrate, requiring only two lines of code to add to existing scripts. It aims to improve collaboration, visibility, and data transparency within ML teams.

Comet Opik is a repository containing two main services: a frontend and a backend. It provides a Python SDK for easy installation. Users can run the full application locally with minikube, following specific installation prerequisites. The repository structure includes directories for applications like Opik backend, with detailed instructions available in the README files. Users can manage the installation using simple k8s commands and interact with the application via URLs for checking the running application and API documentation. The repository aims to facilitate local development and testing of Opik using Kubernetes technology.

The Open MatSci ML Toolkit is a flexible framework for machine learning in materials science. It provides a unified interface to a variety of materials science datasets, as well as a set of tools for data preprocessing, model training, and evaluation. The toolkit is designed to be easy to use for both beginners and experienced researchers, and it can be used to train models for a wide range of tasks, including property prediction, materials discovery, and materials design.

ReproModel is an open-source toolbox designed to boost AI research efficiency by enabling researchers to reproduce, compare, train, and test AI models faster. It provides standardized models, dataloaders, and processing procedures, allowing researchers to focus on new datasets and model development. With a no-code solution, users can access benchmark and SOTA models and datasets, utilize training visualizations, extract code for publication, and leverage an LLM-powered automated methodology description writer. The toolbox helps researchers modularize development, compare pipeline performance reproducibly, and reduce time for model development, computation, and writing. Future versions aim to facilitate building upon state-of-the-art research by loading previously published study IDs with verified code, experiments, and results stored in the system.

DVC, or Data Version Control, is a command-line tool and VS Code extension that helps you develop reproducible machine learning projects. With DVC, you can version your data and models, iterate fast with lightweight pipelines, track experiments in your local Git repo, compare any data, code, parameters, model, or performance plots, and share experiments and automatically reproduce anyone's experiment.

Weights & Biases (W&B) is a platform that helps users build better machine learning models faster by tracking and visualizing all components of the machine learning pipeline, from datasets to production models. It offers tools for tracking, debugging, evaluating, and monitoring machine learning applications. W&B provides integrations with popular frameworks like PyTorch, TensorFlow/Keras, Hugging Face Transformers, PyTorch Lightning, XGBoost, and Sci-Kit Learn. Users can easily log metrics, visualize performance, and compare experiments using W&B. The platform also supports hosting options in the cloud or on private infrastructure, making it versatile for various deployment needs.

KitOps is a packaging and versioning system for AI/ML projects that uses open standards so it works with the AI/ML, development, and DevOps tools you are already using. KitOps simplifies the handoffs between data scientists, application developers, and SREs working with LLMs and other AI/ML models. KitOps' ModelKits are a standards-based package for models, their dependencies, configurations, and codebases. ModelKits are portable, reproducible, and work with the tools you already use.

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Run mock interviews, review content and get tips to ace strategy consulting and investment banking interviews

Run Golang code in ChatGPT

I help create and run productive design thinking sessions.

Download and Run Open Source LLMs Locally.

Your new DM's screen: helping Dungeon Masters to craft & run amazing D&D adventures.

Hosts a real SQLite database and helps you create tables, make schema changes, and run SQL queries, ideal for all levels of database administration.

Meet the Restaurant Startup Guide GPT: your friendly guide in the restaurant biz. It offers casual, approachable advice to help you start and run your own restaurant with ease.

A community-building GPT based on the wildly popular Community Design™ framework from Mighty Networks. Start creating communities that run themselves.

Friendly web assistant for efficient code. Ask the wizard to create an application and you will get the HTML, CSS and Javascript code ready to run your web application.

Find trails that fit your nature for your next hike, ride, or run.

I'm your brainstorm muse in marketing and advertising; the creativity machine you need to sharpen the skills, land the job, generate the ideas, win the pitches, build the brands, ace the awards, or even run your own agency. Psst... don't let your clients find out about me! 😉

Provides running splits for Strava Routes, accounting for distance and elevation changes

Design your perfect race calendar and training plans for your next race.

Creative naming assistant for product developers

Manages office supplies to ensure smooth operations.

Giveaway bot for managing and drawing giveaway winners

「走れメロス」のメロスになりきって回答します。

A helpful coach for guiding teams through Design Sprints with a touch of sass.