Why people are still key to tech transformation

From the mass adoption of ChatGPT to generative AI infiltrating almost every industry (75% of business leaders rank generative AI as the top emerging technology that will impact business over the next 18 months), digital transformation has become inevitable for most organizations.

And while much of the recent conversation surrounding digital transformation has focused on the humans versus robots angle, businesses that ignore their human capital and purely target technology as a means to grow and prosper do so at their peril.

That’s according to Donal Spring, Principal Architect at Red Hat’s Open Innovation Labs. Speaking recently at the Dublin Tech Summit, Spring highlighted that although transformation is inevitable, it needs to be anchored by the people tasked with achieving progression and change, instead of solely focusing on deploying tech at scale to achieve a competitive advantage.

“To be pretty blunt, I’m not a big fan of the big ‘T’ word, the big transformation word. I feel like it means so much to so many people that it can kind of become a little bit toxic,” he said.

“Organizations have been on big underscore transformation projects, whether it’s agile transformation, or DevOps transformation, digital transformations, and really, they need to move it back to what it is we’re actually trying to achieve? And what measures are they setting around those things?”

Spring says that successful transformation is all about the people.

“Adopting some new piece of technology and expecting it to get 10x time to market because it says so on the box is a pretty unrealistic expectation. They need to think about the people and process side of any technology change, because you can’t have one without the other.”

Cross-functional teams that merge talent from various aspects of the business are also crucial, but arguably, talent is just one piece of the puzzle.

According to a recent report by McKinsey, the ability to develop and deploy a clear strategy instead of being reactionary is key. It highlights that focusing on specific areas, for example customer service or internal processes, is the best way to do this.

Either way, the good news for tech professionals is that their technical expertise and soft skills such as critical thinking and communication underpin the kind of digital transformation that can be quantified in terms of growth, profitability and innovation.

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NVIDIA is looking for a computer scientist to work in its Compute Developer Technology (Devtech) team as an AI Developer Technology Engineer. In this role you’ll study and develop cutting-edge techniques in deep learning, graphs, machine learning and data analytics, and perform in-depth analysis and optimization to ensure the best possible performance on current- and next-generation GPU architectures. You will also work directly with key customers to understand the current and future problems they are solving and provide the best AI solutions using GPUs and collaborate closely with the architecture, research, libraries, tools and system software teams at NVIDIA to influence the design of next-generation architectures, software platforms and programming models. View more details here.

Are you passionate about helping organizations manage talent effectively? In this Talent Transformation Strategist role you will use your expertise in management consulting to examine complex systems through an innovation perspective, and make recommendations for improving organizational strategy, structure, human capital and management practices. As a client engagement lead, you will often act as the day-to-day POC for the client, including providing management, administrative, and technical interface between the client and the Booz Allen team. You will also serve as career manager to staff, including providing direct manager guidance, support for career development and oversight to resolving concerns. See the full job description here.

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Red Hat CEO on AI moves and source code kerfuffle

At the Red Hat Summit earlier this year, Red Hat deepened its platform capabilities with OpenShift AI to address the needs of organisations that are set to add more artificial intelligence (AI) workloads into the mix of applications that run on OpenShift.

The move is a natural extension of the company’s goal to be the platform of choice for application developers and infrastructure operators to build and run applications in a distributed IT environment that spans public and private clouds, as well as at the edge of the network.

With OpenShift AI, Red Hat is providing a standardised foundation for creating production AI and machine learning models. It has also teamed up with IBM on Ansible Lightspeed, with Big Blue training its Watson Code Assistant to write Ansible automation playbooks.

Red Hat’s AI moves, however, were somewhat overshadowed by the reaction from the open source community over its decision to limit access to the source code of Red Hat Enterprise Linux (RHEL) to its customers. The decision, announced about a month after the summit, was aimed at preventing rebuilders from profiting from RHEL code without adding value to the software.

In an interview with Computer Weekly, Red Hat CEO Matt Hicks talks up the company’s efforts to support the use of generative AI across the hybrid cloud environment and the competitive landscape for machine learning operations (MLOps) tooling. He also weighs in on the RHEL source code kerfuffle, and how Red Hat is addressing community concerns over the decision.

Could you unpack some of the key announcements at the recent Red Hat Summit and what they mean for the company moving forward?

Hicks: I’ll start with AI and go backwards because I think it has become pretty clear that AI, by nature, is going to be a hybrid workload. You’re probably going to train models in large environments, and then you’re going to run those models as close to your users as you can. We’ve believed in open hybrid cloud for a long time and that’s an exciting workload that gets customers in the hybrid architecture mentality.

Most enterprise customers, because of things like ChatGPT, are trying to figure out the impact of AI on their business. It gets them to think about how they can do hybrid well, and the bulk of their summit announcements is about setting the foundation for hybrid, whether it’s for traditional apps, cloud-native apps or AI workloads.

We do this in a couple of different ways, starting with the secure supply chain work we’re doing. As you know, technology stacks are changing quickly, and so when you’re delivering a foundation, whether it’s on-premise, in public cloud or eventually towards the edge, understanding the provenance of that foundation and knowing that it’s secure is critical, especially as stuff moves out of your datacentre.

Someone can always go into CentOS and all the code is there to recompose, but their preference for Linux distributions is to add something novel or specialised to make a distribution better in ways that were not before versus reproducing their operating system as close as you can Matt Hicks, Red Hat

Service Interconnect is the second piece they announced. It makes it easier for applications to connect to components across the hybrid cloud through SSH [secure shell protocol] tunnels and VPNs [virtual private networks]. We’re really excited about that because they believe that AI is not going to exist by itself – it’ll be running next to applications which have to interconnect from training environments to where your business runs today.

The third piece is the developer hub. We’ve seen many enterprises that use OpenShift build their own portals to collect their assets and point their developers to where they should start, such as the images and services to use. That work is so common that if you have a secure foundation, and you build applications that span multiple locations, being able to publish and consume those to enable broader development teams is equally critical.

What about addressing some of the challenges with AI like explainability, particularly with large language models that have billions, even trillions, of parameters?

Hicks: There are two parts to that – there’s one part they do, and the other part depends on the model creator. I’ll talk about the work they did on Ansible Lightspeed with IBM to deliver domain specific AI generation capabilities where you can ask for a playbook, and we’ll generate it for you. While ChatGPT is very broad, this is very specific and suits Ansible really well.

And to your point, one of the things they highlighted was sourcing, specifically where the AI recommendations came from, because we’re in the business of open source. Licensing, copyright and trademark rules are important – you just can’t take any code you want and put it in any other code. They wanted to make sure they demonstrated what could be possible.

Now where this breaks down is actually across the two stacks. With OpenShift, they help to support a whole class of work in DevOps – source code management, peer review, publishing of code, tagging, knowing your release modules, pipelines and then publishing code. That’s what they do really well in OpenShift. They can take this whole collection of stuff and move code from laptops to production.

AI models are not all that different. In terms of the discipline required, you need to know the model you started from. If it’s generative AI, you need to know exactly what data you brought in and how you trained or did refinement training or prompt engineering. You need to be able to track the output and test against it before you publish it into production, so if a result changes, you know where it came from. This is the tricky part as data changes so quickly that you can’t just publish it and not retrain it. Retraining is going to be as constant as code generation.

So, what they do in OpenShift AI is MLOps – pulling in data, training models, and using very similar pipelines as you would with code. But you need to have a foundation model, and this leads to how the model was trained in the first place, which is something that Red Hat does not do. It’s done by the likes of IBM, Meta, OpenAI and other model generators and within Hugging Face, there’s also a lot of open source model generation.

In the case of IBM, they tightly control their model because it was domain specific to Ansible. They tightly controlled what they trained against so they could drive that core attribution at the end. There are two different camps – some train on everything publicly available, giving you those massive parameter models where attribution will always be a challenge. Then, there’s Hugging Face which has a lot of specialised models that may start with a foundation model but are bounded to domains.

Our goal is to make sure they can add that discipline to what you started with. What did you change in terms of data? How did you retrain? What were the results and where was it published? There’s a lot of training right now, but in the next year or two, they think we’ll move more into the inference space and how you iterate becomes critical.

Are there plans to work with other players in the market apart from IBM? Also, Red Hat has deep relationships with hyperscalers which also have MLOps capabilities – what are your thoughts on the competitive landscape?

Hicks: One of the reasons why they don’t do models is that they want to make sure that we’re a platform company. Their job is to run the best models in the best way possible. How can they use RHEL and OpenShift to bridge a model – whichever one it is – to Nvidia, Intel or AMD hardware to drive training and inference? Not being in the model space makes us a natural partner with everybody and it really becomes a hardware statement. How can they get the most out of the training environment on OpenShift distributed computing, and then inference, which a lot of times comes closer to core RHEL or maybe a smaller OpenShift instance. So, that’s the first layer.

The second layer when they look at OpenShift AI is that they partner with a lot of other companies today that add specialised capabilities, whether it’s Starburst that’s looking at function array and others. It’s exciting to see the work that IBM is doing on Watsonx. They’ve utilised OpenShift AI heavily, but they were comfortable with OpenShift to start with. Their goal is to make sure that as a platform company, they have that neutrality and independence. I’m glad they can serve IBM, but there will be other partners as well because there’s just so much specialisation and niche offerings in this space.

We've had this incredible run and opportunity of making Linux, OpenShift and Ansible successful in the enterprise. But the walls of the datacentre are shifting, and new technologies are changing how enterprises build things. That's their next opportunity to tackle and there's still plenty of work going from datacentres to cloud Matt Hicks, Red Hat

I met with SUSE’s CEO recently and they spoke about the recent decision by Red Hat to limit access to RHEL’s source code to its customers. A lot has been written by Red Hat executives to explain the rationale, but how are you framing the issue for customers and addressing community concerns over the decision?

Hicks: I’ll tackle that in two ways. On the community concerns, I think half of it is people just starting to realise that they brought access to RHEL, whether it’s RHEL for teams or multiple instances available for non-production use or free RHEL available to individuals and hobbyists. Their goal first is, if you are a contributor to Linux, they never want to stand in your way of using their products. And I think we’ve probably removed a lot of those barriers almost a year ago. Is there room to Excellerate as people use RHEL more? Absolutely, and that’s one part of making sure RHEL is available to that audience.

When they get to communities that want to build a specialised Linux or start from some of the work that we’ve done but take it in a different direction, their argument would be that CentOS Stream, in terms of the next version of RHEL, provides you with everything you need. If you want to make more aggressive changes to it, Fedora provides you with everything that you would need. Your contributions there can then flow into RHEL, if they choose.

The bit-for-bit rebuilding of RHEL just doesn’t serve a use case for us. Now, someone can always go into CentOS and all the code is there to recompose, but their preference for Linux distributions is to add something novel or specialised to make a distribution better in ways that were not before versus reproducing their operating system as close as you can.

As for their customers, most of them don’t live in the same world as the community builders. Their source policy with RHEL covers their customer base extremely well, because if you need the source code as a customer, you’ll get them. They have customers that have used RHEL and CentOS, and that’s certainly a decision point for them. But Linux is the most available operating system on the planet, and so they have plenty of options to choose from. We’ll always want to make sure they can serve them with RHEL, but it hasn’t really been a customer challenge.

I’d say the challenge is communities feeling like they took something away from them. And half of that is just not being super familiar with CentOS Stream and not being familiar with the ways that RHEL is available to them. We’ve been in open source for a while and any change you make in open source tends to get really strong reactions. They still hold true to open source – they still open source everything they do, and they still contribute back a tremendous amount for every dollar they make.

In your letter to Red Hat employees about the recent layoffs, you mentioned about the importance of focusing on things that Red Hat does really well. Can you elaborate on what those things are and what you hope to achieve?

Hicks: It’s a great question and I start almost every company meeting saying, ‘Let’s be comfortable that they are a platform company’. We’re going to sit above hardware, and in the world of edge, on new consolidated boxes outside the datacentre. And we’re going to connect that to applications, whether they are traditional applications or new cloud-native apps. And then you’re going to have AI workloads going forward.

Our job, from the RHEL days to middleware with JBoss to OpenShift and distributed computing, is to make sure that developers who want to build with their platform have the widest reach possible. That’s important because there are so many things changing right now. When you look at just the intersection of edge and AI, to be a platform company, they have to serve that market and that class of workload, which means they have to be investing in engineering and sales.

We’ve had this incredible run and opportunity of making Linux, OpenShift and Ansible successful in the enterprise. But the walls of the datacentre are shifting, and new technologies are changing how enterprises build things. That’s their next opportunity to tackle and there’s still plenty of work going from datacentres to cloud. But they have to keep that relentless focus on being a platform and serving those use cases.

That’s what they want to do very well and some of that work is in the operating system space in areas like securing the software supply chain, PyTorch optimisations or Nvidia integration. Some work will go into distributed computing, which is what we’re doing with OpenShift, and there’s a whole lot of work in orchestration around Ansible.

We will certainly invest in areas outside of those three, but if I do my job right, you’ll never see us invest in an area that you can’t pin back to that platform use case. I think that’s a pretty big market for us right now. They know the dynamics of this market, they know how to sell into this market, and they have the talent in this market. There’s enough opportunity in these evolving areas to focus on.

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