In the era of generative AI, vector databases have become indispensable for storing and querying high-dimensional data efficiently. However, like all databases, vector databases are vulnerable to a range of attacks, including cyber threats, phishing attempts, and unauthorized access. This vulnerability is particularly concerning considering that these databases often contain sensitive and��
]]>Get started with NVIDIA NIM for deploying large language models (LLMs). Request access to a free, hands-on lab today.
]]>Speech and translation AI models developed at NVIDIA are pushing the boundaries of performance and innovation. The NVIDIA Parakeet automatic speech recognition (ASR) family of models and the NVIDIA Canary multilingual, multitask ASR and translation model currently top the Hugging Face Open ASR Leaderboard. In addition, a multilingual P-Flow-based text-to-speech (TTS) model won the LIMMITS ��24��
]]>Join us on March 20 for Cybersecurity Developer Day at GTC to gain insights on leveraging generative AI for cyber defense.
]]>An AI agent is a system consisting of planning capabilities, memory, and tools to perform tasks requested by a user. For complex tasks such as data analytics or interacting with complex systems, your application may depend on ?collaboration among different types of agents. For more context, see Introduction to LLM Agents and Building Your First LLM Agent Application. This post explains the��
]]>This week��s model release features NVIDIA cuOpt, a world-record-breaking accelerated optimization engine that helps teams solve complex routing problems and deliver new capabilities. It enables organizations to reimagine logistics, operations research, transportation, and supply chain optimization. NVIDIA cuOpt facilitates many logistics optimization use cases, including: Ultimately��
]]>Discover how generative AI is powering cybersecurity solutions with enhanced speed, accuracy, and scalability.
]]>Central and Eastern Europe (CEE) is quickly gaining recognition as one of the world��s most important rising technology ecosystems. A highly skilled workforce, government support, proximity to key markets, and a history of entrepreneurship are all factors that have led to a significant increase in funding to the region over the past several years. In turn, the increase in funding has led to dozens��
]]>Generative AI has marked an important milestone in the AI revolution journey. We are at a fundamental breaking point where enterprises are not only getting their feet wet but jumping into the deep end. With over 50 frameworks, pretrained models, and development tools, NVIDIA AI Enterprise, the software layer of the NVIDIA AI platform, is designed to accelerate enterprises to the leading edge��
]]>Learn how to use an NVIDIA AI workflow to uniquely fingerprint users and machines across your network in a new, free NVIDIA LaunchPad hands-on lab.
]]>Learn to build an engaging and intelligent virtual assistant with NVIDIA AI workflows powered by NVIDIA Riva in this free hands-on lab from NVIDIA LaunchPad��
]]>NVIDIA Fleet Command announced new features giving IT administrators more advanced controls and protection for edge environments. Unlike traditional data centers with hundreds of servers in a single location, edge deployments have one or two servers in thousands of locations. Traditional IT management tools struggle to meet the needs of these distributed environments, especially when it comes��
]]>Learn how to streamline building state-of-the-art session-based recommender pipelines with this free hands-on lab.
]]>Edge computing and edge AI are powering the digital transformation of business processes. But, as a growing field, there are still many questions about what exactly needs to be in an edge management platform. The benefits of edge computing include low latency for real-time responses, using local area networks for higher bandwidth, and storage at lower costs compared to cloud computing.
]]>Two years ago, NVIDIA and VMware announced that they would reimagine and re-architect the data center. Hundreds of engineers dedicated across each company have worked closely to bring this joint solution to fruition. NVIDIA announces the availability of VMware vSphere on the NVIDIA BlueField DPU, providing the ideal solution for delivering a software-defined��
]]>The convergence of AI and IoT has shifted the center of gravity for data away from the cloud and to the edge of the network. In retail stores, factories, fulfillment centers, and other distributed locations, thousands of sensors are collecting petabytes of data that power insights for innovative AI use cases. Because the most valuable insights are generated at the edge��
]]>Join this webinar and Metropolis meetup on July 20 and 21 to learn how NVIDIA Jetson Orin and NVIDIA Launchpad boost your go-to-market efforts for vision AI applications.
]]>For scalable data center performance, NVIDIA GPUs have become a must-have. NVIDIA GPU parallel processing capabilities, supported by thousands of computing cores, are essential to accelerating a wide variety of applications across different industries. The most compute-intensive applications across diverse industries use GPUs today: Different applications across this spectrum can��
]]>The demand for edge computing is higher than ever, driven by the pandemic, the need for more efficient business processes, as well as key advances in the Internet of Things (IoT), 5G, and AI. In a study published by IBM in May 2021, 94% of surveyed executives said that their organizations will implement edge computing in the next 5 years. Edge AI, the combination of edge computing and AI��
]]>AI adoption has grown rapidly over the past few years due to its ability to automate repetitive tasks and increase revenue opportunities. Yet many companies still struggle with how to meaningfully scale AI in financial services. Increased data needs and a lack of internal talent are a few issues. In this post, we provide a landscape overview of AI use cases and highlight some key scaling��
]]>The new year has been off to a great start with NVIDIA AI Enterprise 1.1 providing production support for container orchestration and Kubernetes cluster management using VMware vSphere with Tanzu 7.0 update 3c, delivering AI/ML workloads to every business in VMs, containers, or Kubernetes. New NVIDIA AI Enterprise labs for IT admins and MLOps are available on NVIDIA LaunchPad��
]]>With the growth of AI applications being deployed at the edge, IT organizations are looking at the best way to deploy and manage their edge computing systems and software. NVIDIA Fleet Command brings secure edge AI to enterprises of any size by transforming NVIDIA-Certified Systems into secure edge appliances and connecting them to the cloud in minutes. In the cloud, you can deploy and manage��
]]>Building production-ready AI applications is hard, especially when starting from scratch. That��s why NVIDIA created Metropolis, a suite of tools to help developers build and bring to market vision AI applications. Deploying these applications in production, especially outside of the data center, can be just as difficult. For many organizations, determining the best way to deploy an��
]]>The newly expanded NVIDIA Metropolis program offers you access to the world��s best development tools and services to reduce the time and cost of managing your vision-AI deployments. Join this developer meetup (dates and times below) with NVIDIA experts to learn five ways the NVIDIA Metropolis program will grow your vision AI business and enhance your go-to-market efforts?. In this meetup��
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