Best AI tools for< Scale Systems >

Millis AI is an instant, natural, and affordable voice AI platform designed for developers to create cutting-edge voice agents with low latency. The platform offers optimized conversation flow handling, affordable accessibility, seamless integration, and scalable expertise. With rates starting at $0.06/min, Millis AI enables users to build human-like voice agents that can manage interruptions and understand human intent. The platform also provides DevOps engineers' expertise in scaling systems for enterprise-level applications.

UnfoldAI is a website offering articles, strategies, and tutorials for building production-grade ML systems. Authored by Simeon Emanuilov, the site covers topics such as deep learning, computer vision, LLMs, programming, MLOps, performance, scalability, and AI consulting. It aims to provide insights and best practices for professionals in the field of machine learning to create robust, efficient, and scalable systems.

Abacus.AI is the world's first AI platform where AI, not humans, build Applied AI agents and systems at scale. Using generative AI and other novel neural net techniques, AI can build LLM apps, gen AI agents, and predictive applied AI systems at scale.

Macgence is an AI training data services platform that offers high-quality off-the-shelf structured training data for organizations to build effective AI systems at scale. They provide services such as custom data sourcing, data annotation, data validation, content moderation, and localization. Macgence combines global linguistic, cultural, and technological expertise to create high-quality datasets for AI models, enabling faster time-to-market across the entire model value chain. With more than 5 years of experience, they support and scale AI initiatives of leading global innovators by designing custom data collection programs. Macgence specializes in handling AI training data for text, speech, image, and video data, offering cognitive annotation services to unlock the potential of unstructured textual data.

ColdIQ is an AI-powered sales prospecting tool that helps B2B companies with revenue above $100k/month to build outbound systems that sell for them. The tool offers end-to-end cold outreach campaign setup and management, email infrastructure setup and warmup, audience research and targeting, data scraping and enrichment, campaigns optimization, sending automation, sales systems implementation, training on tools best practices, sales tools recommendations, free gap analysis, sales consulting, and copywriting frameworks. ColdIQ leverages AI to tailor messaging to each prospect, automate outreach, and flood calendars with opportunities.

Tangram Vision is a company that provides sensor calibration tools and infrastructure for robotics and autonomous vehicles. Their products include MetriCal, a high-speed bundle adjustment software for precise sensor calibration, and AutoCal, an on-device, real-time calibration health check and adjustment tool. Tangram Vision also offers a high-resolution depth sensor called HiFi, which combines high-resolution depth data with high-powered AI capabilities. The company's mission is to accelerate the development and deployment of autonomous systems by providing the tools and infrastructure needed to ensure the accuracy and reliability of sensors.

Keep is an open-source AIOps platform designed for managing alerts and events at scale. It offers features like enrichment, workflows, a single pane of glass, and over 90 integrations. Keep leverages AI technology for IT Operations, providing alert correlation based on past incidents and a continuous feedback loop for performance enhancement. It integrates with monitoring systems, incident response, ticketing, source control, change management, and CMDB systems, offering bidirectional providers for automated alert correlation and workflow automation to reduce noise and alert fatigue.

Privado AI is a privacy engineering tool that bridges the gap between privacy compliance and software development. It automates personal data visibility and privacy governance, helping organizations to identify privacy risks, track data flows, and ensure compliance with regulations such as CPRA, MHMDA, FTC, and GDPR. The tool provides real-time visibility into how personal data is collected, used, shared, and stored by scanning the code of websites, user-facing applications, and backend systems. Privado offers features like Privacy Code Scanning, programmatic privacy governance, automated GDPR RoPA reports, risk identification without assessments, and developer-friendly privacy guidance.

Fieldbox is a digital, data, and AI scale-up partner that helps industrial businesses enhance safety, operational efficiency, and agility through AI solutions. They offer services such as data integration, supply chain optimization, production optimization, and predictive maintenance. Fieldbox builds and operates data-powered industrial solutions for leading companies, ensuring consistent reliability and efficiency worldwide. They provide tailored delivery methods, combining business expertise, technical skills, and delivery management to maximize the value of digital, data, and AI strategies. Unlike point software solutions, Fieldbox allows clients to own and control the algorithms and software developed for them, safeguarding proprietary technology and maintaining a competitive edge.

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.

The Center for AI Safety (CAIS) is a research and field-building nonprofit based in San Francisco. Their mission is to reduce societal-scale risks associated with artificial intelligence (AI) by conducting impactful research, building the field of AI safety researchers, and advocating for safety standards. They offer resources such as a compute cluster for AI/ML safety projects, a blog with in-depth examinations of AI safety topics, and a newsletter providing updates on AI safety developments. CAIS focuses on technical and conceptual research to address the risks posed by advanced AI systems.

Mentals.ai is an operating system for AI agents that enables users to design and deploy complex AI agents using natural language instructions. It focuses on creativity and logic rather than programming, allowing for multi-agent collaboration, advanced features like memory and code execution, and various use cases such as business automation, personal automation, and marketing automation. The platform also supports the creation of intelligent chatbots, virtual employees, and multi-agent systems, revolutionizing productivity and problem-solving in various domains.

GenWorlds is an event-based communication framework for building multi-agent systems. It offers a platform for creating Generative AI applications where users can design customizable environments, utilize scalable architecture, access a repository of memories and tools, choose cognitive processes for agents, and pick coordination protocols. GenWorlds aims to foster a vibrant community of developers, AI enthusiasts, and innovators to collaborate, innovate, share knowledge, and grow together.

Laudio is an intelligent platform designed to help frontline leaders in health systems streamline their work and prioritize high-impact actions with their teams. By integrating essential workflows and leveraging AI technology, Laudio aims to drive large-scale change by saving time, standardizing best practices, and improving employee engagement and patient experience.

Shield AI is a defense technology company building the world's best AI pilot, Hivemind, to enable swarms of drones and aircraft to operate autonomously without GPS, communications, or a pilot. Their mission is to protect service members and civilians with intelligent systems. Hivemind is a top gun for every aircraft, more than just preset behaviors and waypoints. Like a human pilot, Hivemind reads and reacts to the battlefield and does not require GPS, waypoints, or prior knowledge to make decisions. It is the first and only fully autonomous AI pilot deployed in combat since 2018. From indoor building clearance with quadcopters to integrated air defense breach with fixed-wing drones and F-16 dogfights, Hivemind learns and autonomously executes missions. Shield AI also offers V-BAT teams, which enable multiple V-BATs to autonomously execute missions in electronically contested environments while reading and reacting to adversaries, the environment, and the other V-BATs executing the mission. V-BAT is combat-tested and deployed since 2018, and it flies in a class of its own. It's the most tactical, most logistically simple VTOL aircraft in the world, capable of executing group 2 to group 5 mission sets. It is the UAS of choice for US and allied forces. Nova 2 is built for the future fight and has proven its value in close-quarters combat with the most demanding customers in the world – on the most high-profile missions. Hivemind gives Nova 2 full autonomy - no GPS, no comms, no pilot needed.

Plus is an AI-based autonomous driving software company that focuses on developing solutions for driver assist and autonomous driving technologies. The company offers a suite of autonomous driving solutions designed for integration with various hardware platforms and vehicle types, ranging from perception software to highly automated driving systems. Plus aims to transform the transportation industry by providing high-performance, safe, and affordable autonomous driving vehicles at scale.

Tübingen AI Center is a thriving hub for European AI, hosted by the Eberhard Karls University of Tübingen in cooperation with the Max Planck Institute for Intelligent Systems. It comprises 20 world-class machine learning research groups with more than 300 PhD students and Postdocs. The center fosters AI talents by offering education and hands-on experience from elementary school onwards. The Machine Learning Cloud at Tübingen AI Center provides cutting-edge AI research infrastructure, supporting collaborative work and large-scale simulations in ML. Funded by the Federal Ministry of Education and Research and the Ministry of Science, Research and Arts Baden-Württemberg.

Typeface is an Enterprise Generative AI Platform for Marketing & Content Creation. It offers a suite of AI tools for marketing and content creation, empowering users to create personalized, on-brand content at scale. The platform integrates with various apps and systems to streamline workflows and enhance creativity. Typeface has been recognized for its innovative AI solutions and responsible AI practices, making it a trusted choice for industry leaders.

Eigen Technologies is an AI-powered data extraction platform designed for business users to automate the extraction of data from various documents. The platform offers solutions for intelligent document processing and automation, enabling users to streamline business processes, make informed decisions, and achieve significant efficiency gains. Eigen's platform is purpose-built to deliver real ROI by reducing manual processes, improving data accuracy, and accelerating decision-making across industries such as corporates, banks, financial services, insurance, law, and manufacturing. With features like generative insights, table extraction, pre-processing hub, and model governance, Eigen empowers users to automate data extraction workflows efficiently. The platform is known for its unmatched accuracy, speed, and capability, providing customers with a flexible and scalable solution that integrates seamlessly with existing systems.

Coram AI is a modern business video security platform that offers AI-powered solutions for various industries such as warehouse, manufacturing, healthcare, education, and more. It provides advanced features like gun detection, productivity alerts, facial recognition, and safety alerts to enhance security and operations. Coram AI's flexible architecture allows users to seamlessly integrate with any IP camera and scale effortlessly to meet their demands. With natural language search capabilities, users can quickly find relevant footage and improve decision-making. Trusted by local businesses and Fortune 500 companies, Coram AI delivers real business value through innovative AI tools and reliable customer support.

At Nesa, privacy is a critical objective. Equivariant Encryption (EE) is a solution developed to perform inference on neural networks without exposing input and output data. EE integrates specialized transformations for neural networks, maintaining data privacy while ensuring inference operates correctly on encrypted inputs. It provides the same latency as plaintext inference with no slowdowns and offers strong security guarantees. EE avoids the computational costs of traditional Homomorphic Encryption (HE) by preserving non-linear neural functions. The tool is designed for modern neural architectures, ensuring accuracy, scalability, and compatibility with existing pipelines.

Nesa is a tool that allows users to run on-prem AI for a fraction of the cost through a blind API. It provides blind privacy, zero latency on protected inference, wide model coverage, cost savings compared to cloud and on-prem AI, RAG support, and ChatGPT compatibility. Nesa achieves blind AI through Equivariant Encryption (EE), a new security technology that provides complete inference encryption with no additional latency. EE allows users to perform inference on neural networks without exposing the underlying data, preserving data privacy and security.

AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.

AITreasureBox is a comprehensive collection of AI tools and resources designed to simplify and accelerate the development of AI projects. It provides a wide range of pre-trained models, datasets, and utilities that can be easily integrated into various AI applications. With AITreasureBox, developers can quickly prototype, test, and deploy AI solutions without having to build everything from scratch. Whether you are working on computer vision, natural language processing, or reinforcement learning projects, AITreasureBox has something to offer for everyone. The repository is regularly updated with new tools and resources to keep up with the latest advancements in the field of artificial intelligence.

The xLSTM-jax repository contains code for training and evaluating the xLSTM model on language modeling using JAX. xLSTM is a Recurrent Neural Network architecture that improves upon the original LSTM through Exponential Gating, normalization, stabilization techniques, and a Matrix Memory. It is optimized for large-scale distributed systems with performant triton kernels for faster training and inference.

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-AIOps is a curated list of academic researches and industrial materials related to Artificial Intelligence for IT Operations (AIOps). It includes resources such as competitions, white papers, blogs, tutorials, benchmarks, tools, companies, academic materials, talks, workshops, papers, and courses covering various aspects of AIOps like anomaly detection, root cause analysis, incident management, microservices, dependency tracing, and more.

The 'awesome-LLM-AIOps' repository is a curated list of academic research and industrial materials related to Large Language Models (LLM) and Artificial Intelligence for IT Operations (AIOps). It covers various topics such as incident management, log analysis, root cause analysis, incident mitigation, and incident postmortem analysis. The repository provides a comprehensive collection of papers, projects, and tools related to the application of LLM and AI in IT operations, offering valuable insights and resources for researchers and practitioners in the field.

NanoFlow is a throughput-oriented high-performance serving framework for Large Language Models (LLMs) that consistently delivers superior throughput compared to other frameworks by utilizing key techniques such as intra-device parallelism, asynchronous CPU scheduling, and SSD offloading. The framework proposes nano-batching to schedule compute-, memory-, and network-bound operations for simultaneous execution, leading to increased resource utilization. NanoFlow also adopts an asynchronous control flow to optimize CPU overhead and eagerly offloads KV-Cache to SSDs for multi-round conversations. The open-source codebase integrates state-of-the-art kernel libraries and provides necessary scripts for environment setup and experiment reproduction.

Nuitrack™ is an ultimate 3D body tracking solution developed by 3DiVi Inc. It enables body motion analytics applications for virtually any widespread depth sensors and hardware platforms, supporting a wide range of applications from real-time gesture recognition on embedded platforms to large-scale multisensor analytical systems. Nuitrack provides highly-sophisticated 3D skeletal tracking, basic facial analysis, hand tracking, and gesture recognition APIs for UI control. It offers two skeletal tracking engines: classical for embedded hardware and AI for complex poses, providing a human-centric spatial understanding tool for natural and intelligent user engagement.

The LLM Twin Course is a free, end-to-end framework for building production-ready LLM systems. It teaches you how to design, train, and deploy a production-ready LLM twin of yourself powered by LLMs, vector DBs, and LLMOps good practices. The course is split into 11 hands-on written lessons and the open-source code you can access on GitHub. You can read everything and try out the code at your own pace.

FastAgency is a powerful tool that leverages the AutoGen framework to quickly build applications with multi-agent workflows. It supports various interfaces like ConsoleUI and MesopUI, allowing users to create interactive applications. The tool enables defining workflows between agents, such as students and teachers, and summarizing conversations. FastAgency aims to expand its capabilities by integrating with additional agentic frameworks like CrewAI, providing more options for workflow definition and AI tool integration.

This repository contains the source code for a GEMM & deep learning hardware accelerator system used to validate proposed systolic array hardware architectures implementing efficient matrix multiplication algorithms to increase performance-per-area limits of GEMM & AI accelerators. Achieved results include up to 3× faster CNN inference, >2× higher mults/multiplier/clock cycle, and low area with high clock frequency. The system is specialized for inference of non-sparse DNN models with fixed-point/quantized inputs, fully accelerating all DNN layers in hardware, and highly optimizing GEMM acceleration.

This repository contains the source code for the SIGIR 2023 paper 'Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisited'. It provides resources for evaluating foundation, transferable, multi-modal, and LLM recommendation models, along with datasets, pre-trained models, and training strategies for IDRec and MoRec using in-batch debiased cross-entropy loss. The repository also offers large-scale datasets, code for SASRec with in-batch debias cross-entropy loss, and information on joining the lab for research opportunities.

Bittensor is an internet-scale neural network that incentivizes computers to provide access to machine learning models in a decentralized and censorship-resistant manner. It operates through a token-based mechanism where miners host, train, and procure machine learning systems to fulfill verification problems defined by validators. The network rewards miners and validators for their contributions, ensuring continuous improvement in knowledge output. Bittensor allows anyone to participate, extract value, and govern the network without centralized control. It supports tasks such as generating text, audio, images, and extracting numerical representations.

This repository is a collection of papers and resources related to recommendation systems, focusing on foundation models, transferable recommender systems, large language models, and multimodal recommender systems. It explores questions such as the necessity of ID embeddings, the shift from matching to generating paradigms, and the future of multimodal recommender systems. The papers cover various aspects of recommendation systems, including pretraining, user representation, dataset benchmarks, and evaluation methods. The repository aims to provide insights and advancements in the field of recommendation systems through literature reviews, surveys, and empirical studies.

The Next-Generation LLM-based Recommender Systems Survey is a comprehensive overview of the latest advancements in recommender systems leveraging Large Language Models (LLMs). The survey covers various paradigms, approaches, and applications of LLMs in recommendation tasks, including generative and non-generative models, multimodal recommendations, personalized explanations, and industrial deployment. It discusses the comparison with existing surveys, different paradigms, and specific works in the field. The survey also addresses challenges and future directions in the domain of LLM-based recommender systems.

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