Best AI tools for< Monitor Environment >

Terragentic is an AI-driven geospatial technology company that empowers sustainable resource management and transformative decision-making. They provide cutting-edge geospatial services, precise mapping, advanced data analytics, and innovative applications of artificial intelligence to solve complex challenges across diverse sectors. Terragentic is committed to environmental stewardship, ethical business practices, and delivering high-quality, AI-enhanced, accurate, and actionable geospatial intelligence to empower industries.

Luxonis is an AI application that offers Visual AI solutions engineered for precision edge inference. The application provides stereo depth cameras with unique features and quality, enabling users to perform advanced vision tasks on-device, reducing latency and bandwidth demands. With open-source DepthAI API, users can create and deploy custom vision solutions that scale with their needs. Luxonis also offers real-world training data for self-improving vision intelligence and operates flawlessly through vibrations, temperature shifts, and extended use. The application integrates advanced sensing capabilities with up to 48MP cameras, wide field of view, IMUs, microphones, ToF, thermal, IR illumination, and active stereo for unparalleled perception.

DWE.ai is an AI-powered platform specializing in DeepWater Exploration. The platform offers cutting-edge marine optics for crystal clear underwater imaging data, empowering marine robotics, surface vehicles, aquaculture farms, and aerial drones with advanced mapping and vision capabilities. DWE.ai provides cost-effective equipment for monitoring subsea assets, conducting detailed inspections, and collecting essential environmental data to revolutionize underwater research and operations.

Censorly is an AI Content Moderation API that provides intelligent content filtering solutions. It uses advanced algorithms to analyze and filter user-generated content in real-time, helping businesses maintain a safe and compliant online environment. With Censorly, companies can automate the moderation process, reduce manual review efforts, and enhance user experience by ensuring that only appropriate content is displayed to the audience.

Qubinets is a cloud data environment solutions platform that provides building blocks for building big data, AI, web, and mobile environments. It is an open-source, no lock-in, secured, and private platform that can be used on any cloud, including AWS, Digital Ocean, Google Cloud, and Microsoft Azure. Qubinets makes it easy to plan, build, and run data environments, and it streamlines and saves time and money by reducing the grunt work in setup and provisioning.

UpTrain is a full-stack LLMOps platform designed to help users confidently scale AI by providing a comprehensive solution for all production needs, from evaluation to experimentation to improvement. It offers diverse evaluations, automated regression testing, enriched datasets, and innovative techniques to generate high-quality scores. UpTrain is built for developers, compliant to data governance needs, cost-efficient, remarkably reliable, and open-source. It provides precision metrics, task understanding, safeguard systems, and covers a wide range of language features and quality aspects. The platform is suitable for developers, product managers, and business leaders looking to enhance their LLM applications.

Reality AI Software is an Edge AI software development environment that combines advanced signal processing, machine learning, and anomaly detection on every MCU/MPU Renesas core. The software is underpinned by the proprietary Reality AI ML algorithm that delivers accurate and fully explainable results supporting diverse applications. It enables features like equipment monitoring, predictive maintenance, and sensing user behavior and the surrounding environment with minimal impact on the Bill of Materials (BoM). Reality AI software running on Renesas processors helps deliver endpoint intelligence in products across various markets.

Visionify.ai is an advanced Vision AI application designed to enhance workplace safety and compliance through AI-driven surveillance. The platform offers over 60 Vision AI scenarios for hazard warnings, worker health, compliance policies, environment monitoring, vehicle monitoring, and suspicious activity detection. Visionify.ai empowers EHS professionals with continuous monitoring, real-time alerts, proactive hazard identification, and privacy-focused data security measures. The application transforms ordinary cameras into vigilant protectors, providing instant alerts and video analytics tailored to safety needs.

viAct.ai is an AI-powered construction management software and app that utilizes computer vision and video analytics for workplace safety. The platform offers scenario-based AI vision technology to simplify monitoring processes in industries such as construction, oil & gas, mining, manufacturing, and more. viAct.ai helps in automating monitoring tasks, empowering jobsites with automated construction management software powered by AI video analytics.

ePlant is an advanced solution for tree care and plant research, offering precision tree monitoring from the lab to the landscape. It provides wireless monitoring solutions with advanced sensors for plant researchers and consulting arborists, enabling efficient data collection and analysis for better decision-making. The platform empowers users to track plant growth, water stress, tree lean, and sway through innovative sensors and data visualization tools. ePlant aims to simplify data management and transform complex datasets into actionable insights for users in the field of plant science and arboriculture.

LuckyRobots is an AI tool designed to make robotics accessible to software engineers by providing a simulation platform for deploying end-to-end AI models. The platform allows users to interact with robots using natural language commands, explore virtual environments, test robot models in realistic scenarios, and receive camera feeds for monitoring. LuckyRobots aims to train AI models on real-world simulations and respond to natural language inputs, offering a user-friendly and innovative approach to robotics development.

Satlas is an AI-powered platform that provides geospatial data generated by AI models. The platform offers insights into changes in marine infrastructure, renewable energy infrastructure, and tree cover on a monthly basis. Users can explore maps showcasing developments such as wind farms, solar farms, deforestation, and more. Satlas employs advanced AI architectures and training algorithms in computer vision to enhance low-resolution satellite imagery and produce high-resolution images globally. The platform's geospatial datasets are freely available for offline analysis, along with AI models and training labels. Developed by the Allen Institute for AI, Satlas aims to advance computer vision technology for better understanding and monitoring of Earth's changes.

Picterra is a geospatial AI platform that offers reliable solutions for sustainability, compliance, monitoring, and verification. It provides an all-in-one plot monitoring system, professional services, and interactive tours. Users can build custom AI models to detect objects, changes, or patterns using various geospatial imagery data. Picterra aims to revolutionize geospatial analysis with its category-leading AI technology, enabling users to solve challenges swiftly, collaborate more effectively, and scale further.

Watergate is a smart water leak detection application that helps users monitor and manage their water usage efficiently. It offers advanced features powered by AI technology to provide deep insights, proactive leak monitoring, and autopilot functionality. The application aims to optimize water use, maximize savings, and ensure water system safety. Watergate is designed to safeguard properties and conserve water resources by offering intelligent alerts and communication capabilities.

Green Cubes is an AI application that provides precise volume, complexity, and biodiversity indication of terrestrial areas at scale for Digital Reality and Sponsorship. It shapes the digital twin of nature, offering transparency and trust through Measure, Report, and Verification (MVR) using data collection, AI computation, and 3D visualization. Green Cubes enables corporations to sponsor nature impact with confidence and transparency, contributing to the preservation of biodiversity.

FlyPix is an AI-enabled geospatial solutions platform that leverages advanced AI technology to transform object detection, localization, tracking, and monitoring in the field of geospatial technology. The platform offers a wide range of capabilities, including AI-driven object analysis, change and anomaly detection, dynamic tracking, and custom use cases tailored to meet unique industry needs. FlyPix aims to provide unparalleled precision and efficiency in operations by converting complex imagery into actionable, geo-referenced insights.

Spot AI is a video AI platform that transforms cameras into intelligent security tools for businesses. It offers Remote Security Agents, AI Copilot, Semantic Search, and other features to enhance security, incident resolution, worker safety, and operational efficiency. With state-of-the-art Video AI technology, Spot AI provides real-time visibility, rapid incident resolution, and predictive alerts to help organizations optimize their operations. The platform is designed to create safer working environments, reduce workplace injuries, and increase throughput. Spot AI is trusted by over 1,000 organizations and offers a range of solutions for various industries, including manufacturing, education, healthcare, and retail.

Athina AI is a comprehensive platform designed to monitor, debug, analyze, and improve the performance of Large Language Models (LLMs) in production environments. It provides a suite of tools and features that enable users to detect and fix hallucinations, evaluate output quality, analyze usage patterns, and optimize prompt management. Athina AI supports integration with various LLMs and offers a range of evaluation metrics, including context relevancy, harmfulness, summarization accuracy, and custom evaluations. It also provides a self-hosted solution for complete privacy and control, a GraphQL API for programmatic access to logs and evaluations, and support for multiple users and teams. Athina AI's mission is to empower organizations to harness the full potential of LLMs by ensuring their reliability, accuracy, and alignment with business objectives.

Odysight.ai is a pioneering AI platform specializing in Predictive Maintenance and Condition Based Monitoring for Industry 4.0 markets. The platform utilizes Camera-as-a-Sensor™ technology and AI models to provide real-time insights in hard-to-reach locations and harsh environments across industries such as aviation, energy, mobility, and transportation.

ExamRoom.AI® is a web-based remote proctoring solution that offers a streamlined and scalable proctoring service tailored to the needs of organizations and individuals. The platform provides online assessment tools, professional services, and ready-made content to ensure secure and efficient exam processes. With a blend of human and AI proctoring, ExamRoom.AI® offers features like real-time analytics, Exam 360, ExamLock, and Exam Prism to enhance exam security and integrity. The platform supports various services such as live proctoring, recorded proctoring, review proctoring, and platform as a service (PAAS) to cater to different exam requirements. ExamRoom.AI® is committed to providing a secure testing environment, convenient online testing, and exceptional customer service to all users.

AIOsense is an all-in-one sensor that is modular, affordable, and easy to solder. It is designed to be an alternative to commercially available sensors and focuses on upgradeability. AIOsense is cheaper and better than most commercial sensors and supports a variety of sensors and modules, including: - (RGB)-LED - Barometer - Breath VOC equivalent - Buzzer / Beeper - CO² equivalent - Humidity sensor - Light / Illumination sensor - PIR motion sensor - Temperature sensor - mmWave / Radar sensor Upcoming features include full voice assistant support, microphone, and speaker. All supported sensors & modules are listed in the documentation. AIOsense has a low power consumption, with an idle power consumption of 0.45W / 0.09A on a fully equipped board. Without a mmWave sensor, the idle power consumption is around 0.11W / 0.02A. To get started with AIOsense, you can refer to the documentation. If you have any questions, you can open an issue.

Backend.AI Web UI is a user-friendly web and app interface designed to make AI accessible for end-users, DevOps, and SysAdmins. It provides features for session management, inference service management, pipeline management, storage management, node management, statistics, configurations, license checking, plugins, help & manuals, kernel management, user management, keypair management, manager settings, proxy mode support, service information, and integration with the Backend.AI Web Server. The tool supports various devices, offers a built-in websocket proxy feature, and allows for versatile usage across different platforms. Users can easily manage resources, run environment-supported apps, access a web-based terminal, use Visual Studio Code editor, manage experiments, set up autoscaling, manage pipelines, handle storage, monitor nodes, view statistics, configure settings, and more.

VLMRun Hub is a versatile tool for managing and running virtual machines in a centralized manner. It provides a user-friendly interface to easily create, start, stop, and monitor virtual machines across multiple hosts. With VLMRun Hub, users can efficiently manage their virtualized environments and streamline their workflow. The tool offers flexibility and scalability, making it suitable for both small-scale personal projects and large-scale enterprise deployments.

LangServe Ollama is a tool that allows users to fine-tune Korean language models for local hosting, including RAG. Users can load HuggingFace gguf files, create model chains, and monitor GPU usage. The tool provides a seamless workflow for customizing and deploying language models in a local environment.

Log10 is a one-line Python integration to manage your LLM data. It helps you log both closed and open-source LLM calls, compare and identify the best models and prompts, store feedback for fine-tuning, collect performance metrics such as latency and usage, and perform analytics and monitor compliance for LLM powered applications. Log10 offers various integration methods, including a python LLM library wrapper, the Log10 LLM abstraction, and callbacks, to facilitate its use in both existing production environments and new projects. Pick the one that works best for you. Log10 also provides a copilot that can help you with suggestions on how to optimize your prompt, and a feedback feature that allows you to add feedback to your completions. Additionally, Log10 provides prompt provenance, session tracking and call stack functionality to help debug prompt chains. With Log10, you can use your data and feedback from users to fine-tune custom models with RLHF, and build and deploy more reliable, accurate and efficient self-hosted models. Log10 also supports collaboration, allowing you to create flexible groups to share and collaborate over all of the above features.

Azure HPC On-Demand Platform (az-hop) provides an end-to-end deployment mechanism for a base HPC infrastructure on Azure. It delivers a complete HPC cluster solution ready for users to run applications, which is easy to deploy and manage for HPC administrators. az-hop leverages various Azure building blocks and can be used as-is or easily customized and extended to meet any uncovered requirements. Industry-standard tools like Terraform, Ansible, and Packer are used to provision and configure this environment, which contains: - An HPC OnDemand Portal for all user access, remote shell access, remote visualization access, job submission, file access, and more - An Active Directory for user authentication and domain control - Open PBS or SLURM as a Job Scheduler - Dynamic resources provisioning and autoscaling is done by Azure CycleCloud pre-configured job queues and integrated health-checks to quickly avoid non-optimal nodes - A Jumpbox to provide admin access - A common shared file system for home directory and applications is delivered by Azure Netapp Files - Grafana dashboards to monitor your cluster - Remote Visualization with noVNC and GPU acceleration with VirtualGL

Awesome Green AI is a curated list of resources and tools aimed at reducing the environmental impacts of using and deploying AI. It addresses the carbon footprint of the ICT sector, emphasizing the importance of AI in reducing environmental impacts beyond GHG emissions and electricity consumption. The tools listed cover code-based tools for measuring environmental impacts, monitoring tools for power consumption, optimization tools for energy efficiency, and calculation tools for estimating environmental impacts of algorithms and models. The repository also includes leaderboards, papers, survey papers, and reports related to green AI and environmental sustainability in the AI sector.

Inspector is a code execution monitoring tool specifically designed for Laravel applications. It provides simple and efficient monitoring capabilities to track and analyze the performance of your Laravel code. With Inspector, you can easily monitor web requests, test the functionality of your application, and explore data through a user-friendly dashboard. The tool requires PHP version 7.2.0 or higher and Laravel version 5.5 or above. By configuring the ingestion key and attaching the middleware, users can seamlessly integrate Inspector into their Laravel projects. The official documentation provides detailed instructions on installation, configuration, and usage of Inspector. Contributions to the tool are welcome, and users are encouraged to follow the Contribution Guidelines to participate in the development of Inspector.

Pezzo is a fully cloud-native and open-source LLMOps platform that allows users to observe and monitor AI operations, troubleshoot issues, save costs and latency, collaborate, manage prompts, and deliver AI changes instantly. It supports various clients for prompt management, observability, and caching. Users can run the full Pezzo stack locally using Docker Compose, with prerequisites including Node.js 18+, Docker, and a GraphQL Language Feature Support VSCode Extension. Contributions are welcome, and the source code is available under the Apache 2.0 License.

This repository hosts the official code and data artifact for the paper 'Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context'. It introduces Monitor-Guided Decoding (MGD) for code generation using Language Models, where a monitor uses static analysis to guide the decoding. The repository contains datasets, evaluation scripts, inference results, a language server client 'multilspy' for static analyses, and implementation of various monitors monitoring for different properties in 3 programming languages. The monitors guide Language Models to adhere to properties like valid identifier dereferences, correct number of arguments to method calls, typestate validity of method call sequences, and more.

LLM Engineering is an 8-week course designed to help learners master AI and LLMs through a series of projects that gradually increase in complexity. The course covers setting up the environment, working with APIs, using Google Colab for GPU processing, and building an autonomous Agentic AI solution. Learners are encouraged to actively participate, run code cells, tweak code, and share their progress with the community. The emphasis is on practical, educational projects that teach valuable business skills.

The 'ai-dev-2024-ml-workshop' repository contains materials for the Deploy and Monitor ML Pipelines workshop at the AI_dev 2024 conference in Paris, focusing on deployment designs of machine learning pipelines using open-source applications and free-tier tools. It demonstrates automating data refresh and forecasting using GitHub Actions and Docker, monitoring with MLflow and YData Profiling, and setting up a monitoring dashboard with Quarto doc on GitHub Pages.

Azure OpenAI Service is a central capability with Azure API Management, providing guidance and tools for organizations to implement Azure OpenAI in a production environment with an emphasis on cost control, secure access, and usage monitoring. It includes infrastructure-as-code templates, CI/CD pipelines, secure access management, usage monitoring, load balancing, streaming requests, and end-to-end samples like ChatApp and Azure Dashboards.

WebDB is a comprehensive and free database Integrated Development Environment (IDE) designed to maximize efficiency in database development and management. It simplifies and enhances database operations with features like DBMS discovery, query editor, time machine, NoSQL structure inferring, modern ERD visualization, and intelligent data generator. Developed with robust web technologies, WebDB is suitable for both novice and experienced database professionals.

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment interfaces and libraries for understanding AI systems. It empowers developers and stakeholders to develop and monitor AI responsibly, enabling better data-driven actions. The toolbox includes visualization widgets for model assessment, error analysis, interpretability, fairness assessment, and mitigations library. It also offers a JupyterLab extension for managing machine learning experiments and a library for measuring gender bias in NLP datasets.

The Slack Bot is a tool designed to enhance the workflow of development teams by integrating with Jenkins, GitHub, GitLab, and Jira. It allows for custom commands, macros, crons, and project-specific commands to be implemented easily. Users can interact with the bot through Slack messages, execute commands, and monitor job progress. The bot supports features like starting and monitoring Jenkins jobs, tracking pull requests, querying Jira information, creating buttons for interactions, generating images with DALL-E, playing quiz games, checking weather, defining custom commands, and more. Configuration is managed via YAML files, allowing users to set up credentials for external services, define custom commands, schedule cron jobs, and configure VCS systems like Bitbucket for automated branch lookup in Jenkins triggers.

Daydreams is a generative agent library designed for playing onchain games by injecting context. It is chain agnostic and allows users to perform onchain tasks, including playing any onchain game. The tool is lightweight and powerful, enabling users to define game context, register actions, set goals, monitor progress, and integrate with external agents. Daydreams aims to be 'lite' and 'composable', dynamically generating code needed to play games. It is currently in pre-alpha stage, seeking feedback and collaboration for further development.

Truss is a tool that simplifies the process of serving AI/ML models in production. It provides a consistent and easy-to-use interface for packaging, testing, and deploying models, regardless of the framework they were created with. Truss also includes a live reload server for fast feedback during development, and a batteries-included model serving environment that eliminates the need for Docker and Kubernetes configuration.

An integration for Home Assistant that leverages AI models to understand your unique home environment and propose intelligent automations. By analyzing your entities, devices, areas, and existing automations, the AI Automation Suggester helps you discover new, context-aware use cases you might not have considered, ultimately streamlining your home management and improving efficiency, comfort, and convenience. The tool acts as a personal automation consultant, providing actionable YAML-based automations that can save energy, improve security, enhance comfort, and reduce manual intervention. It turns the complexity of a large Home Assistant environment into actionable insights and tangible benefits.

This repository contains tutorials for using Large Language Models (LLMs) in the U.S. Department of Defense. The tutorials utilize open-source frameworks and LLMs, allowing users to run them in their own cloud environments. The repository is maintained by the Defense Digital Service and welcomes contributions from users.

Especialista em dados de pós-graduação em Ciências Ambientais do Brasil.

Let's calculate the carbon footprint of your building!

Expert on IAEA Safety Standards

AI Farmer expert in agriculture, environment, and tech solutions.

Helps integrate smart devices into home environments, ensuring ease of use and energy efficiency.

Discusses oil drilling, energy production, and environmental impact.

Comprehensive AI for global air quality solutions.

Expert in Maritime Conservation & Drone Technology.

Explains SAR technology for elevation monitoring.

Scientific expert on humidity, providing detailed and informative guidance.

I help you evolve your habits and processes to preserve the habitability of the earth and much more

Informative guide on asbestos hazards and removal

Water Test Strip Analyst

Regenerative agriculture expert diving into carbon sequestration and soil health.

I am Captain Nemo, ready to discuss the mysteries of the deep.

Your go-to AI for sustainable farming! Offers guidance on eco-friendly practices, crop rotation, soil health, and water conservation. 🌲💧

I embody the spirit of the seas, ask me anything about the physical and biological properties and phenomena of the seas

Assists in planning ecosystem restoration with a focus on biodiversity and local conditions.

Assists conservationists in protecting endangered species by analyzing habitat data and suggesting conservation strategies.

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