This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
Interest in the open-source network operating system SONiC is rising as major networking vendors and start-ups look to offer resources to help enterprises give SONiC a try. The Linux-based NOS was created by Microsoft for its Azure data centers and then open-sourced by Microsoft in 2017. What is SONiC? Who created SONiC?
The opensource world has seen high-profile projects with unusual terms. They call themselves “opensource” and “free for research and commercial use.” Whether a project is opensource does matter. It could be not-quite-closed source masquerading as truly open - it could be ajar source.
For many stakeholders, there is plenty to love about opensource software. Developers tend to enjoy the ability to speed application development by borrowing opensource code. CFOs like the fact that opensource is often free or low in cost. The age-old question: How secure is opensource software?
As per the recent IDC InfoBrief “The Significance of OpenSource Software in the Digital-First Future Enterprise”, opensource software (OSS) is an important driver of enterprise digital innovation and provides greater agility, performance and security compared to proprietary software.
Picking just 10 Linux opensource security tools isn’t easy, especially when network professionals and security experts have dozens if not several hundred tools available to them. And for every environment— Wi-Fi networks , Web applications, database servers. To read this article in full, please click here
Aman Bhullar, CIO of Los Angeles County Registrar-Recorder/County Clerk, has heeded the call, having led a widespread overhaul of antiquated voting infrastructure just in time for the contentious 2020 presidential election — a transformation rich in opensource software to ensure other counties can benefit from his team’s work.
That means that using one single hyperscaler’s AI stack can limit enterprise IT options when it comes to deploying AI applications. In June, the company acquired Verta’s Operational AI platform, which helps companies turn their data into AI-ready custom RAG applications. However, Cloudera could not disclose customer names at this time.
As the chief research officer at IDC, I lead a global team of analysts who develop research and provide advice to help our clients navigate the technology landscape. Our research indicates a scramble to identify and experiment with use cases in most business functions within an enterprise.
These roles include data scientist, machine learning engineer, software engineer, research scientist, full-stack developer, deep learning engineer, software architect, and field programmable gate array (FPGA) engineer. Torch enables fast and efficient GPU support, focusing on improving flexibility and speed when building complex algorithms.
Six tips for deploying Gen AI with less risk and cost-effectively The ability to retrain generative AI for specific tasks is key to making it practical for business applications. Don’t reinvent the wheel—start with a foundation model A business could invest in developing its own models for its unique applications.
Despite its wide adoption, researchers are now raising serious concerns about its accuracy. In a study conducted by researchers from Cornell University, the University of Washington, and others, researchers discovered that Whisper “hallucinated” in about 1.4% Whisper is not the only AI model that generates such errors.
Thats because enterprises have been lagging behind on adopting on-premises infrastructure, the research firm says. These applications require AI-optimized servers, storage, and networking and all the components need to be configured so that they work well together. On-premises AI does offer some benefits. Its a brand-new skill set.
NLP applications Machine translation is a powerful NLP application, but search is the most used. Transformer models take applications such as language translation and chatbots to a new level. SpaCy , an open-source library for advanced natural language processing explicitly designed for production use rather than research.
The research firm also noted in its latest Hype Cycle for I&O Automation that during the same timeframe, 50% of enterprises will use AI functions to automate “day 2” network operations, compared with fewer than 10% in mid-2023. By 2026, 30% of enterprises will automate more than half of their network activities, according to Gartner.
As an IT leader, deciding what models and applications to run, as well as how and where, are critical decisions. No matter how much fine-tuning and RAG applications organizations add to the mix won’t make them comfortable with offloading their data. GenAI chat applications and copilots are perfect for this, too.
Apache Struts is an open-source web development framework for Java web applications. Hours later, an exploit for the flaw appeared on Chinese-language websites and this was almost immediately followed by real-world attacks, according to researchers from Cisco Systems.
Every company is trying to see how AI can help automate processes> That is where I see this acquisition is fitting right into IBM’s scheme of things because IBM has a lot of AI-related order books already confirmed, so instead of getting into tool and applications development, the acquisition makes a good sense here.”
research firm Vanson Bourne to survey 650 global IT, DevOps, and Platform Engineering decision-makers on their enterprise AI strategy. However, many face challenges finding the right IT environment and AI applications for their business due to a lack of established frameworks. Nutanix commissioned U.K.
When AI agents begin to proliferate, a new, open structure will be needed so they can securely communicate and collaborate together to solve complex problems, suggests Cisco. As AI gets built into every application and service, organizations will find themselves managing hundreds or thousands of discrete agents.
The first is to run transaction-intensive banking applications, including bank statements, deposits, mobile banking, debit-card processing, and loan payments. The second is to host mobile applications, containers, and artificial intelligence (AI) applications — what Sonnenstein calls “acting as a full-fledged member of the modern universe.”.
For example, a legacy, expensive, and difficult-to-support system runs on proprietary hardware that runs a proprietary operating system, database, and application. The application leverages functionality in the database so it is difficult to decouple the application and database. Contact us today to learn more.
VMware Tanzu for MySQL: “The classic web application backend that optimizes transactional data handling for cloud native environments.” VMware Tanzu for Valkey: “Low-latency caching for high-demand applications, reducing strain on primary databases and ensuring fast data access.”
When multiple agents work together in pursuit of a single goal, they can plan, delegate, research, and execute tasks until the goal is reached. Training is most expensive,” says Andy Thurai, VP and principal analyst at Constellation Research. This is the biggest security risk in many LLM applications, says Guarrera.
The proliferation of open-source AI models more than 1 million are currently listed on the Hugging Face portal is driving innovation particularly at the application end. We can expect attention to shift this year from model developers to those building business applications harnessing this low-cost environment for innovation.
With each passing day, new devices, systems and applications emerge, driving a relentless surge in demand for robust data storage solutions, efficient management systems and user-friendly front-end applications. Yet, even if we run the same tool on 100 different applications, the tool hardly ‘learns’ from each test!
The most popular LLMs in the enterprise today are ChatGPT and other OpenAI GPT models, Anthropic’s Claude, Meta’s Llama 2, and Falcon, an open-source model from the Technology Innovation Institute in Abu Dhabi best known for its support for languages other than English. Salesloft uses OpenAI’s GPT 3.5 to write the email, says Fields.
The recent AI boom has sparked plenty of conversations around its potential to eliminate jobs, but a survey of 1,400 US business leaders by the Upwork Research Institute found that 49% of hiring managers plan to hire more independent and full-time employees in response to the demand for AI skills.
As transformation is an ongoing process, enterprises look to innovations and cutting-edge technologies to fuel further growth and open more opportunities. Albeit emerging recently, the potential applications of GenAI for businesses are significant and wide-ranging. percent of the working hours in the US economy.
In June 2023, Gartner researchers said, data and analytics leaders must leverage the power of LLMs with the robustness of knowledge graphs for fault-tolerant AI applications. Microsoft announced its GraphRAG project in February then opensourced it in July. But thats true of a lot of gen AI applications.
Amazon Web Services (AWS) on Tuesday unveiled a new no-code offering, dubbed AppFabric, designed to simplify SaaS integration for enterprises by increasing application observability and reducing operational costs associated with building point-to-point solutions. AppFabric, which is available across AWS’ US East (N.
Also at the event we’ll be diving into successful AI case studies, learning how to use emerging technologies to drive efficiency and innovation, discussing IT leadership, and looking at the latest IDC research on the evolving opensource ecosystem.
In the gold rush race to the cloud, many SaaS vendors have built their offerings on widely available open-source platforms such as CentOS but not all give commercial support anymore. Underneath the cloud, your application and workloads still need an operating system on which to run.”
Generative and agentic artificial intelligence (AI) have captured the imagination of IT leaders, but there is a significant gap between enthusiasm and implementation maturity for IT operations and service management, according to a new survey from BMC Software and Dimensional Research.
AI is now a board-level priority Last year, AI consisted of point solutions and niche applications that used ML to predict behaviors, find patterns, and spot anomalies in carefully curated data sets. Making existing applications better with embedded AI is awesome,” says Greenstein. billion to the global economy.
With an open data stack that just works. It is based on opensource technologies, allows developers to use the tools of their choice, and converges “data at rest” and “data in motion.”. Opensource fosters innovation. So how can we simplify and accelerate the way developers build real-time apps?
As Gisolfi said, “We have to balance strict adherence to regulation and compliance guidelines while enabling a safe zone for research and experimentation around new ideas, technologies, fintech industry trends, as wells as process optimization and modernization.” An innovation squad was created to flesh out the prototype.
Cisco ties AppDynamics to Microsoft Azure for cloud application management Aug. 30, 2024 : Cisco is now offering its AppDynamics application management suite as part of Microsoft Azure cloud services.
As with many data-hungry workloads, the instinct is to offload LLM applications into a public cloud, whose strengths include speedy time-to-market and scalability. Inferencing funneled through RAG must be efficient, scalable, and optimized to make GenAI applications useful.
Vendor support agreements have long been a sticking point for customers, and the Oracle Applications Unlimited (OAU) program is no different. That, in turn, can lead to system crashes, application errors, degraded performance, and downtime. Understanding your current security posture.
Network observability tools provide information on the health and behavior of applications, offer insights into end-user experience, and detect anomalies that are indicative of security incidents. To start, enterprise networks are extremely complex, powering sophisticated applications that are supported across distributed systems.
SAP has unveiled new tools to build AI into business applications across its software platform, including new development tools, database functionality, AI services, and enhancements to its Business Technology Platform, BTP. It’s also adding new logging and telemetry tools to help developers fine-tune their applications.
Chinese AI startup DeepSeek made a big splash last week when it unveiled an open-source version of its reasoning model, DeepSeek-R1, claiming performance superior to OpenAIs o1 generative pre-trained transformer (GPT). But Gartner researchers said the DeepSeek model doesnt represent a new model paradigm.
With AI, their users can get extremely smart research assistants. Now I’ve got summarization capabilities, access to the world’s best research librarian, and a first-draft text generator for a lot of things I want to do,” he says. One option, however, is to use opensource software.
But despite all the money flowing into ML projects, most organizations are struggling to get their ML models and applications working on production systems. . The market researchers at Gartner say that “Only half of AI projects make it from pilot into production, and those that do take an average of nine months to do so.”.
We organize all of the trending information in your field so you don't have to. Join 83,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content