Red Hat Summit 2023: The Biggest Announcements

Ansible Lightspeed with IBM Watson Code Assistant, the Red Hat Developer Hub and Red Hat Trusted Application Pipeline and Trusted Content are some of the biggest announcements to come out of Red Hat Summit 2023 this week.

Generative artificial intelligence for Ansible. An enterprise-grade, unified, open developer hub. And new services in the Red Hat Trusted Software Supply Chain suite.

Ansible Lightspeed with IBM Watson Code Assistant, the Red Hat Developer Hub and Red Hat Trusted Application Pipeline and Trusted Content are some of the biggest announcements to come out of Red Hat Summit 2023 this week.

Red Hat, the Raleigh, N.C.-based IBM subsidiary and open software vendor, runs the event from Tuesday to Thursday in Boston.

[RELATED: New Dell-Red Hat Infrastructure Blocks Promise To Slash Networking OpEx] 

Summit 2023 comes soon after the vendor announced plans to lay off upwards of 800 employees, about 4 percent of its global workforce – suggesting that innovation has continued despite the lower headcount.

Red Hat CEO Matt Hicks promised no cuts in sales and engineering and said the layoffs were due to the vendor needing to “rebalance” and decide what offerings were priority. The layoffs appear to have targeted project managers and customer success professionals.

Hicks also sought a cultural change with employees “willing to engage and learn the intersections between our teams and to manage them more directly, with fewer layers of interface.”

Some of the other news to come out of Summit 2023 include:

*An Event-Driven Ansible Offering

*Updated capabilities for Red Hat Insights for RHEL

*Red Hat Advanced Cluster Security Cloud Service

About 80 percent of Red Hat’s overall sales come through the channel, according to CRN’s 2023 Channel Chiefs. In the 2022 edition, Red Hat disclosed that it had 7,000 worldwide channel partners, 1,500 of them in North America.

IBM has 55,000 channel partners worldwide, with 12,000 of them in North America, according to CRN’s 2023 Channel Chiefs.

These are the biggest announcements from Red Hat Summit 2023.

Wade Tyler Millward is an associate editor covering cloud computing and the channel partner programs of Microsoft, IBM, Red Hat, Oracle, Salesforce, Citrix and other cloud vendors. He can be reached at wmillward@thechannelcompany.com.

Red Hat Creates Recipe For Production-Ready AI Models

A woman examines a TV dinner box she has taken from the freezer. (Photo by �� William ... [+] Gottlieb/CORBIS/Corbis via Getty Images)

There are many types of Ops. Deriving its name from the operations function now commonly written as Ops, we classify this software engineering discipline as a component of the many types of Ops now working to keep our computing cloud up in the air and running.

There is DevOps that connects to developers, there is FinOps for operations with an eye on how much systems costi to run, DevSecOps for security, DataOps for… well, you guessed it, data-centric operations, then there are NetOps for networking requirements, MLOps and AIOps when operations has a meaty portion of intelligence directed at it to make it more self-service through the mining of large data sets so we know more about what’s happening inside any given system… and finally perhaps, there’s NoOps when all of the above coalesce to completely automate the entire Ops universe.

Focused on the MLOps end of the scale by virtue of its work in the infrastructure and open source operating system space from the start, Red Hat has now extended its Red Hat OpenShift AI service. This is the company’s Artificial Intelligence (AI) and Machine Learning operations (MLOps) foundation. But there’s a lot of Red Hat product nomenclature at play here, so let’s lay down some clarifications.

In other words, Red Hat started with cloud management, added Machine Learning and now more holistically looks after the lifecycle and lifespan of the AI resulting from the whole process.

As the use of Large Language Models (LLMs) like GPT-4 becomes increasingly mainstream, software application developers in all workplace domains and industry verticals are now exploring ways to exploit these ‘foundation model’ technologies. This term refers to core AI models built by being trained on massive data sets that are often extended by proprietary use cases, tuned and skewed by exposure to domain-specific data (to align to say, the automobile industry, for example) to make them more accurate to their specific use cases. Foundational AI models can be commercial or open source

Red Hat reminds us that the initial training of AI models is incredibly infrastructure intensive, requiring specialized platforms and tools even before serving, tuning and model management are taken into consideration. This means that in many cases, organizations are often limited in how they can actually use AI/ML. Alongside the fact that many businesses are more concerned about aspects of their IT stack such as data quality and application integration, adopting AI for Ops (or even adopting AI at all) is often still an afterthought for many.

“Foundation models provide real, tangible benefits to enterprises when it comes to harnessing the benefits of AI, but they still require investment in training and fine-tuning to meet the unique needs of an enterprise,” said Chris Wright, chief technology officer and senior vice president for global engineering at Red Hat. “Red Hat’s vision for enterprise AI builds on this existing reality with Red Hat OpenShift AI, which provides a single, flexible and scalable foundation to train, maintain, fine-tune and actually use foundational models in production. OpenShift AI is still OpenShift, meaning that IT organizations trust it and understand it, providing a single platform that stretches from today’s needs to tomorrow’s.”

The company says that Red Hat OpenShift AI provides a standardized foundation for creating production AI/ML models, as well as running the resulting applications. It delivers the consistency and cloud-to-edge (edge being the Internet of Things) deployment options of Red Hat OpenShift. Platform engineers can create scalable configurations, specific to the needs of their data scientists and developers. OpenShift AI can be used to fine-tune existing models, saving on initial model training costs, increasing time to value and expanding AI usage beyond specialized data scientists.

“Red Hat OpenShift AI provides several optional technology partner offerings including Anaconda Commercial Edition, IBM Watson Studio, Intel OpenVINO and AI Analytics Toolkit, Pachyderm and Starburst Galaxy. It also provides access to 30 additional AI/ML-certified partners as part of the OpenShift ecosystem,” notes the company, in a technical product document.

Customers with specific regulatory and compliance requirements, including air-gapped and disconnected environments, can prepare data, and develop, train and deploy models on-premises using OpenShift AI. Customers can also develop models in the public cloud, and deploy them on-premises or at the edge using the same consistent tooling and interfaces, providing a unique hybrid MLOps environment that enables collaboration between IT operations, data science and application developers.

Ultimately, Red Hat says that OpenShift AI provides customers with a proven foundation that can reliably scale to train foundation models using OpenShift’s native GPU acceleration features on-premises or via a cloud service. Red Hat OpenShift AI already fuels the next-generation of foundational models by underpinning the generative AI services of IBM WatsonX.ai, IBM’s AI platform designed to scale intelligent applications and services. Red Hat OpenShift AI provides the base for these AI advancements, which include IBM Watson Code Assistant to deliver domain-specific AI to your IT organization and developer teams.

The trends surfacing here are not just the application of AI and ML into the Ops practice, that’s pretty much a given. The wider almost more cerebral change here is the way we’re talking about how AI/ML models are initially built, developed and changed, monitored and cleaned for bias and ultimately engineered to be able to take advantage of the speed of high-end processors like Graphical Processing Units (GPUs).

Now that we live in a world where AI is becoming ubiquitous, it should be no surprise to find that it also has a lifecycle and a size factor on its journey towards being scaled and used more extensively. Red Hat wants to empower software engineering teams working with these intelligence streams and may one day start talking about Data Science Developers (DSDs) as a formalized job function.

In the meantime, the smart money is on living breathing AI.