Nvidia has unveiled its Enterprise version 2.1, an update to the company's end-to-end artificial intelligence and machine learning workloads software. The updates affect the Nvidia TAO Toolkit and Nvidia Rapids, with further support being added for Red Hat OpenShift running in the public cloud. The company says this should "[make] enterprise AI even more accessible across hybrid or multi-cloud environments," with Microsoft Azure NVads A10 v5 series virtual machines also gaining certification. REST APIs integration, pre-trained weights import, TensorBoard integration, and new pre-trained models are some of the highlights coming to the latest iteration of Nvidia TAO Toolkit, version 22.05, which itself is a low code solution of Nvidia TAO. The tool is designed to make building computer vision and speech recognition models easier. New models, techniques, and data processing capabilities added to Nvidia RAPIDS 22.04 will provide "more support for data workflows," which will be available across all of the data science libraries.

NVIDIA AI Enterprise 2.1 is now generally available for all enterprise users. Today, the global technology leader NVIDIA announced the most advanced version of its AI-powered data and analytics software for enterprise users. The new AI suite would enable users to fully-optimize their IT and Low Code DevOps processes in a highly scalable AI-based environments. These include applications across bare metal, virtual, container, and Cloud environments. The latest NVIDIA AI Enterprise 2.1 is part of NVIDIA's AI enterprise suite.

You don't have to look far to see what's at the root of enterprise IT's enthusiasm for artificial intelligence (AI) and machine learning (ML) projects – data, and lots of it! Data, indeed, is king across a range of industries, and companies need AI/ML to glean meaningful insights from it. HCA Healthcare, for example, used machine learning to create a big data analysis platform to speed sepsis detection, while BMW used it to support its automated vehicle initiatives. While AI/ML can bring tremendous value to businesses, your team will first have to navigate around a common set of challenges. Get the eBook: Top considerations for building a production-ready AI/ML environment.

Red Hat Inc., a provider of open source solutions, today announced new certifications and capabilities for Red Hat OpenShift aimed at accelerating the delivery of intelligent applications across the hybrid cloud. These enhancements, including the certification of Red Hat OpenShift with NVIDIA AI Enterprise 2.0, as well as the general availability of Red Hat OpenShift 4.10, are intended to help organizations deploy, manage and scale artificial intelligence (AI) workloads with confidence. According to Gartner, worldwide artificial intelligence (AI) software revenue is forecast to total $62.5 billion in 2022, an increase of 21.3% from 2021.1 As enterprises integrate AI and machine learning capabilities into cloud-native applications to deliver more insight and customer value, they need a more agile, flexible and scalable platform for developing and deploying ML models and intelligent applications into production more quickly. Red Hat OpenShift is engineered to provide this foundation and, with today's updates, Red Hat OpenShift makes it easier for organizations to add AI workloads to the industry's leading enterprise Kubernetes platform. While AI is transforming how enterprises do business, operationalizing an AI infrastructure can be complex and time- and resource-intensive.

NEW YORK AND ARMONK, N.Y., Oct. 11, 2021 – IBM (NYSE: IBM) and Deloitte today announced a new offering--DAPPER, an AI-enabled managed analytics solution. The solution reinforces the two organizations' 21-year global alliance--which helps organizations accelerate the adoption of hybrid cloud and AI across the enterprise--and 10 years of experience implementing the Deloitte Analytics Platform. DAPPER's end-to-end capabilities will allow organizations to gain confidence in the insights that their data provides via a secured, simple to consume managed service offering that aims to resolve the challenges of adopting AI. Relevant and actionable data can catapult companies to success in today's competitive, insights-driven business environment. Clients across industries report they are struggling to accelerate the value of AI and analytics--due to lack of trust in data, domain expertise, and the resources to create a solution that can work across business environments--while simultaneously meeting strict security and compliance requirements.

The tool is designed for those looking to integrate and run AI and ML technologies across cloud environments. IBM is announcing a new addition to its open-source Cloud-Native Toolkit that will allow developers to integrate their AI and ML applications "to cloud-native environments and optimize scalable, reliable deployments." Saishruthi Swaminathan, Carlos Santana and Sepideh Seifzadeh -- members of the IBM Center for Open-Source Data & AI Technologies team -- explained the effort in a blog post, noting that it was becoming necessary to integrate and run AI and ML technologies across cloud environments. Last year, the team released the Elyra AI toolkit and said the latest launch is a machine-learning, end-to-end pipeline starter kit within the Cloud-Native Toolkit. "Using critical hybrid cloud capabilities including open source and Red Hat OpenShift, developers can use the new toolkit as a starting point to transition their ML and AI-powered applications from Jupyter notebooks to production environments," the IBM team wrote.

When EY Global Chairman and CEO Carmine Di Sibio broke bread with IBM Chairman and CEO Arvind Krishna last July at a luncheon meeting, the two companies were more "frenemies" than partners. It was not a great relationship," said Di Sibio. "We were more competing than we were friends." That all changed when Krishna laid out IBM's new partner ecosystem charge that was taking hold in the wake of IBM's blockbuster acquisition of Red Hat. "The message I got at lunch was IBM was changing, [going through a] transformation, and the Red Hat acquisition was a big piece of this," he said. Di Sibio was impressed and as a result has made a big bet on Krishna and IBM. Now, the New York-based $37.2 billion global consulting powerhouse is aiming to drive $1 billion in revenue from the IBM partnership over the next few years. Di Sibio said he has been heartened by the speed at which Krishna is driving the transformation at IBM. "IBM notoriously has been, I'll say, moving slower," he said. "I do think they have changed, and they are changing. I have confidence they are going to move fast." The Red Hat deal, in fact, has changed the "culture" at IBM and the ecosystem strategy for the better, said Di Sibio. "Their change in strategy really enabled us to have a different type of relationship," he said. Key to building a strong partnership has been Krishna's technology savvy as a leader, his partnership commitment and the trust that has developed between the two executives, said Di Sibio. "Arvind is pretty technical," he said. "I think he is the right choice for where their strategy is going as they move forward." Since that lunch meeting, EY and IBM have combined on a joint go-to-market plan centered on the IBM Financial Services Cloud, combining EY's financial consulting muscle with IBM's cloud prowess. The two companies also launched just two months ago EY Diligence Edge, an AI-enabled M&A due diligence platform hosted on IBM Cloud and supported by IBM Watson Discovery. EY had opportunities to use different cloud providers for EY Diligence Edge but chose IBM because of its hybrid cloud strategy and Watson AI technology as a "differentiator," said Di Sibio. He said the IBM technology is helping win M&A customers for EY. "I think Arvind is bringing IBM back to being an innovative technology company based on hybrid cloud," he said. The EY partnership is just one piece of Krishna's bold bet on partners with the company's biggest go-to-market change in three decades as part of his "maniacal focus" to make IBM the No. 1 provider of hybrid cloud and AI. "I think it's the biggest change we have made in our go-to-market [model] in my living memory," said Krishna, who started his career at IBM in 1990 as a researcher at its Thomas J. Watson Research Center. "If you think about how we pay our people and how we have got clarity on the partners, it is the single biggest change in 30 years on the go-to-market.

If you are NLOps and AIOps analysts, this news will delight you. Data science platform cnvrg.io is now available through Red Hat Marketplace. IBM and Red Hat announced the launch of the world's most powerful open source Hybrid Cloud innovation accelerator, Red Hat Marketplace, a gallery of enterprise applications for global customers. Through the marketplace, customers can take advantage of responsive support, streamlined billing and contracting, simplified governance, and single-dashboard visibility across clouds. This collaboration can help data scientists and IT organizations to operationalize their machine learning and dramatically accelerate time to production." ML platform helps data scientists spend less time on DevOps, with advanced MLOps solutions, data version control and management, experiment tracking, model management, model monitoring and more. Its collaborative end-to-end solution enables teams to streamline machine learning and quickly deploy high impact models. Expert and novice data scientists enjoy the flexibility to use any tool, language or environment while maintaining full control of their models in training and in production. Built in collaboration with Red Hat and IBM, Red Hat Marketplace is designed to meet the unique needs of developers, procurement teams and IT leaders through simplified and streamlined access to popular enterprise software. All solutions available through the marketplace have been tested and certified for Red Hat OpenShift, the industry's most comprehensive enterprise Kubernetes platform, allowing them to run anywhere OpenShift runs. A containers-based approach helps ensure that applications can be run and managed the exact same way, regardless of the underlying cloud infrastructure. This gives companies the flexibility to run their workloads on-premises or in any public or private cloud with improved portability and confidence that their applications and data are protected against vendor lock-in. Sandesh Bhat, IBM General Manager, Open Cloud Technology & Applications said, "Through Red Hat Marketplace, we're expanding our ecosystem together with partners like cnvrg.io and helping our customers thrive in a hybrid multi-cloud world." Sandesh added, "Container-based environments are the future of enterprise technology, and Red Hat OpenShift is the industry's most comprehensive enterprise Kubernetes platform.

Fans can become instant "experts" about the players and the tournament match-ups with new AI-powered insights. This year, IBM is partnering again with the United States Tennis Association (USTA) and has developed three new tennis-based digital experiences for fans of the US Open. Spectators won't be allowed at the Arthur Ashe Stadium at the Billie Jean King National Tennis Center in Flushing, NY when the Grand Slam event begins on Aug. 31, due to the COVID-19 pandemic, but they will be able to participate remotely with new fan experiences that use artificial intelligence (AI) underpinned by hybrid cloud technologies. IBM has partnered with the USTA for 29 years, but 2018 was the first year that AI-powered tools were used by players and coaches. Last year, IBM introduced the IBM Coach Advisor and IBM Watson OpenScale.

Palo Alto Networks has released next-generation firewall (NGFW) software that integrates machine learning to help protect enterprise traffic to and from hybrid clouds, IoT devices and the growing numbers of remote workers. The machine learning is built into the latest version of Palo Alto's firewall operating system – PAN 10.0 – to prevent real-time signatureless attacks and to quickly identify new devices – in particular IoT products – with behavior-based identification. NGFWs include traditional firewall protections like stateful packet inspection but add advanced security judgments based on application, user and content. "Security attacks are continually morphing at rapid pace and traditional signature-based security approaches cannot keep up with the millions of new devices, running a variety of operating systems and software stacks coming on the network," said Anand Oswal senior vice president and GM at Palo Alto. "IoT devices, which are growing exponentially, exacerbated that issue because they have so many of their own different agents, patches and OS's it's impossible to set security policies around them." Oswal said the ML in its new NGFW uses inline machine-learning models to identify variants of known attacks as well as many unknown cyberthreats to prevent up to 95% of zero-day malware in real time.