Best AI tools for< Monitor Ml Security >

Protect AI is a comprehensive platform designed to secure AI systems by providing visibility and manageability to detect and mitigate unique AI security threats. The platform empowers organizations to embrace a security-first approach to AI, offering solutions for AI Security Posture Management, ML model security enforcement, AI/ML supply chain vulnerability database, LLM security monitoring, and observability. Protect AI aims to safeguard AI applications and ML systems from potential vulnerabilities, enabling users to build, adopt, and deploy AI models confidently and at scale.

Cyguru is an all-in-one cloud-based AI Security Operation Center (SOC) that offers a comprehensive range of features for a robust and secure digital landscape. Its Security Operation Center is the cornerstone of its service domain, providing AI-Powered Attack Detection, Continuous Monitoring for Vulnerabilities and Misconfigurations, Compliance Assurance, SecPedia: Your Cybersecurity Knowledge Hub, and Advanced ML & AI Detection. Cyguru's AI-Powered Analyst promptly alerts users to any suspicious behavior or activity that demands attention, ensuring timely delivery of notifications. The platform is accessible to everyone, with up to three free servers and subsequent pricing that is more than 85% below the industry average.

Fiddler AI is an AI Observability platform that provides tools for monitoring, explaining, and improving the performance of AI models. It offers a range of capabilities, including explainable AI, NLP and CV model monitoring, LLMOps, and security features. Fiddler AI helps businesses to build and deploy high-performing AI solutions at scale.

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.

Leapmax is a workforce analytics software designed to enhance operational efficiency by improving employee productivity, ensuring data security, facilitating communication and collaboration, and managing compliance. The application offers features such as productivity management, data security, remote team collaboration, reporting management, and network health monitoring. Leapmax provides advantages like AI-based user detection, real-time activity tracking, remote co-browsing, collaboration suite, and actionable analytics. However, some disadvantages include the need for employee monitoring, potential privacy concerns, and dependency on internet connectivity. The application is commonly used by contact centers, outsourcers, enterprises, and back offices. Users can perform tasks like productivity monitoring, app usage tracking, communication and collaboration, compliance management, and remote workforce monitoring.

Plat.AI is an automated predictive analytics software that offers model building solutions for various industries such as finance, insurance, and marketing. It provides a real-time decision-making engine that allows users to build and maintain AI models without any coding experience. The platform offers features like automated model building, data preprocessing tools, codeless modeling, and personalized approach to data analysis. Plat.AI aims to make predictive analytics easy and accessible for users of all experience levels, ensuring transparency, security, and compliance in decision-making processes.

VoteMinecraftServers is a modern Minecraft voting website that provides real-time live analytics to help users stay ahead in the game. It utilizes advanced AI and ML technologies to deliver accurate and up-to-date information. The website is free to use and offers a range of features, including premium commands for enhanced functionality. VoteMinecraftServers is committed to data security and user privacy, ensuring a safe and reliable experience.

Arize AI is an AI observability tool designed to monitor and troubleshoot AI models in production. It provides configurable and sophisticated observability features to ensure the performance and reliability of next-gen AI stacks. With a focus on ML observability, Arize offers automated setup, a simple API, and a lightweight package for tracking model performance over time. The tool is trusted by top companies for its ability to surface insights, simplify issue root causing, and provide a dedicated customer success manager. Arize is battle-hardened for real-world scenarios, offering unparalleled performance, scalability, security, and compliance with industry standards like SOC 2 Type II and HIPAA.

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.

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.

Striveworks is an AI application that offers a Machine Learning Operations Platform designed to help organizations build, deploy, maintain, monitor, and audit machine learning models efficiently. It provides features such as rapid model deployment, data and model auditability, low-code interface, flexible deployment options, and operationalizing AI data science with real returns. Striveworks aims to accelerate the ML lifecycle, save time and money in model creation, and enable non-experts to leverage AI for data-driven decisions.

PoplarML is a platform that enables the deployment of production-ready, scalable ML systems with minimal engineering effort. It offers one-click deploys, real-time inference, and framework agnostic support. With PoplarML, users can seamlessly deploy ML models using a CLI tool to a fleet of GPUs and invoke their models through a REST API endpoint. The platform supports Tensorflow, Pytorch, and JAX models.

Hopsworks is an AI platform that offers a comprehensive solution for building, deploying, and monitoring machine learning systems. It provides features such as a Feature Store, real-time ML capabilities, and generative AI solutions. Hopsworks enables users to develop and deploy reliable AI systems, orchestrate and monitor models, and personalize machine learning models with private data. The platform supports batch and real-time ML tasks, with the flexibility to deploy on-premises or in the cloud.

JFrog ML is an AI platform designed to streamline AI development from prototype to production. It offers a unified MLOps platform to build, train, deploy, and manage AI workflows at scale. With features like Feature Store, LLMOps, and model monitoring, JFrog ML empowers AI teams to collaborate efficiently and optimize AI & ML models in production.

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.

Comet ML is a machine learning platform that integrates with your existing infrastructure and tools so you can manage, visualize, and optimize models—from training runs to production monitoring.

Comet ML is a machine learning platform that integrates with your existing infrastructure and tools so you can manage, visualize, and optimize models—from training runs to production monitoring.

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.

ClearML is an open-source, end-to-end platform for continuous machine learning (ML). It provides a unified platform for data management, experiment tracking, model training, deployment, and monitoring. ClearML is designed to make it easy for teams to collaborate on ML projects and to ensure that models are deployed and maintained in a reliable and scalable way.

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.

Awesome-LLM is a curated list of resources related to large language models, focusing on papers, projects, frameworks, tools, tutorials, courses, opinions, and other useful resources in the field. It covers trending LLM projects, milestone papers, other papers, open LLM projects, LLM training frameworks, LLM evaluation frameworks, tools for deploying LLM, prompting libraries & tools, tutorials, courses, books, and opinions. The repository provides a comprehensive overview of the latest advancements and resources in the field of large language models.

Invariant Analyzer is an open-source scanner designed for LLM-based AI agents to find bugs, vulnerabilities, and security threats. It scans agent execution traces to identify issues like looping behavior, data leaks, prompt injections, and unsafe code execution. The tool offers a library of built-in checkers, an expressive policy language, data flow analysis, real-time monitoring, and extensible architecture for custom checkers. It helps developers debug AI agents, scan for security violations, and prevent security issues and data breaches during runtime. The analyzer leverages deep contextual understanding and a purpose-built rule matching engine for security policy enforcement.

This repository is a curated list of open-source libraries for production large language models. It includes tools for data preprocessing, training/finetuning, evaluation/benchmarking, serving/inference, application/RAG, testing/monitoring, and guardrails/security. The repository also provides a new category called LLM Cookbook/Examples for showcasing examples and guides on using various LLM APIs.

Awesome Algorand is a curated list of resources related to the Algorand Blockchain, including official resources, wallets, blockchain explorers, portfolio trackers, learning resources, development tools, DeFi platforms, nodes & consensus participation, subscription management, security auditing services, blockchain bridges, oracles, name services, community resources, Algorand Request for Comments, metrics and analytics services, decentralized voting tools, and NFT marketplaces. The repository provides a comprehensive collection of tools, tutorials, protocols, and platforms for developers, users, and enthusiasts interested in the Algorand ecosystem.

A curated list of 50+ resources to help you become a Generative AI Data Scientist. This repository includes resources on building GenAI applications with Large Language Models (LLMs), and deploying LLMs and GenAI with Cloud-based solutions.

The LLM Engineer's Handbook is an official repository containing a comprehensive guide on creating an end-to-end LLM-based system using best practices. It covers data collection & generation, LLM training pipeline, a simple RAG system, production-ready AWS deployment, comprehensive monitoring, and testing and evaluation framework. The repository includes detailed instructions on setting up local and cloud dependencies, project structure, installation steps, infrastructure setup, pipelines for data processing, training, and inference, as well as QA, tests, and running the project end-to-end.

LLM App Stack, also known as Emerging Architectures for LLM Applications, is a comprehensive list of available tools, projects, and vendors at each layer of the LLM app stack. It covers various categories such as Data Pipelines, Embedding Models, Vector Databases, Playgrounds, Orchestrators, APIs/Plugins, LLM Caches, Logging/Monitoring/Eval, Validators, LLM APIs (proprietary and open source), App Hosting Platforms, Cloud Providers, and Opinionated Clouds. The repository aims to provide a detailed overview of tools and projects for building, deploying, and maintaining enterprise data solutions, AI models, and applications.

The AI Enablement Stack is a curated collection of venture-backed companies, tools, and technologies that enable developers to build, deploy, and manage AI applications. It provides a structured view of the AI development ecosystem across five key layers: Agent Consumer Layer, Observability and Governance Layer, Engineering Layer, Intelligence Layer, and Infrastructure Layer. Each layer focuses on specific aspects of AI development, from end-user interaction to model training and deployment. The stack aims to help developers find the right tools for building AI applications faster and more efficiently, assist engineering leaders in making informed decisions about AI infrastructure and tooling, and help organizations understand the AI development landscape to plan technology adoption.

Awesome AI is a curated list of artificial intelligence resources including courses, tools, apps, and open-source projects. It covers a wide range of topics such as machine learning, deep learning, natural language processing, robotics, conversational interfaces, data science, and more. The repository serves as a comprehensive guide for individuals interested in exploring the field of artificial intelligence and its applications across various domains.

Awesome Responsible AI is a curated list of academic research, books, code of ethics, courses, data sets, frameworks, institutes, newsletters, principles, podcasts, reports, tools, regulations, and standards related to Responsible, Trustworthy, and Human-Centered AI. It covers various concepts such as Responsible AI, Trustworthy AI, Human-Centered AI, Responsible AI frameworks, AI Governance, and more. The repository provides a comprehensive collection of resources for individuals interested in ethical, transparent, and accountable AI development and deployment.

Awesome European Tech is an up-to-date list of recommended European projects and companies curated by the community to support and strengthen the European tech ecosystem. It focuses on privacy and sustainability, showcasing companies that adhere to GDPR compliance and sustainability standards. The project aims to highlight and support European startups and projects excelling in privacy, sustainability, and innovation to contribute to a more diverse, resilient, and interconnected global tech landscape.

QuestCameraKit is a collection of template and reference projects demonstrating how to use Meta Quest’s new Passthrough Camera API (PCA) for advanced AR/VR vision, tracking, and shader effects. It includes samples like Color Picker, Object Detection with Unity Sentis, QR Code Tracking with ZXing, Frosted Glass Shader, OpenAI vision model, and WebRTC video streaming. The repository provides detailed instructions on how to run each sample and troubleshoot known issues. Users can explore various functionalities such as converting 3D points to 2D image pixels, detecting objects, tracking QR codes, applying custom shader effects, interacting with OpenAI's vision model, and streaming camera feed over WebRTC.

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

ChatGPT is a large language model that can generate human-like text, translate languages, write different kinds of creative content, and answer your questions in a conversational way. It is trained on a massive amount of text data, and it is able to understand and respond to a wide range of natural language prompts. Here are 5 jobs suitable for this tool, in lowercase letters: 1. content writer 2. chatbot assistant 3. language translator 4. creative writer 5. researcher

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Seismic and volcanic monitor with in-depth data and visuals.

Frost Protection System is an AI bot optimizing open field farming of fruits, vegetables, and flowers, combining real-time data and AI to boost yield, cut costs, and foster sustainable practices in a user-friendly interface.

Your SRE, DevOps and Observability buddy

A specialist in DataOps and Data Observability, aiding in data management and monitoring.

EUR/ZAR analysis with real-time rate and current news focus

stunspot's advisor for all things Financial Cybersec

Expert in Broad Financial Compliance

Specializes in Anti-Money Laundering/Counter-Financing of Terrorism compliance and analysis.

Advanced DevOps Guru with Linux distro and cloud-native tech expertise.

Facilitates Socratic dialogues to refine your network state.

Expert in Maritime Conservation & Drone Technology.

A bot that provides detailed cybersecurity threat intelligence.

Ensures product quality through systematic process monitoring and evaluation.

Global Conflict Analyst that will provide a 'wartime update' on the worst global conflict atm.

Monitoring tools expert with brief responses and guiding hints.

Ensures efficient and effective network performance and security.

Critically examines government actions and policies

Comprehensive AI for global air quality solutions.