Make things open: it makes things better

MLOps.WTF Edition #18

Ahoy there 🚢,

This episode is brought to you by Tom Stockton, CEO & Co Founder at Fuzzy Labs.

Last month we sponsored the Manchester Tech Festival and were fortunate to hear the Manchester Mayor, Andy Burnham, talk about his vision for digital adoption in Manchester and the wider region.

He said something really quite powerful:

“Young people in the boroughs can see the skyscrapers from their bedroom windows, but they don’t know the pathways to work in them.”

I couldn’t get it out of my head and it gave me an idea about how open source could help with this mission…

I’m an open source nerd, and the solutions we build at Fuzzy Labs are rooted in open source. I wrote a LinkedIn post about how if our government focused on creating open source foundations for running digital public services, it wouldn’t just be cheaper and more efficient, it would also be more transparent. It could literally open doors for young people. Showing them how the software that runs our country is built, allowing them to learn from it, and maybe even contribute to it.

The post clearly struck a chord, so I thought I’d follow up with something longer. I wanted to look at how this could work in practice, and share some examples of where other countries have made open source a success. But when I dug deeper, I found something surprising. The UK is already doing this in some brilliant ways!

(Robbie with Andy Burnham and a bottle of our MLOps Open Sauce)

What we’re doing well

Have you ever noticed how UK government websites all look and feel the same? That’s because they’re built on an open source foundation called the GOV.UK Design System.

It’s a project run by the Government Digital Service (GDS), and it’s used across hundreds of government services including HMRC, DWP, DEFRA, and the NHS.

Beyond the open source code, it’s a community with a mailing list, monthly calls, design days and a Slack channel. It has all the ingredients of a great open source project. Collaboration, openness, and a sense of shared ownership.

The result is that every time you visit a government website, you get a consistent and accessible experience. Departments don’t need to reinvent buttons, forms, or navigation every time they build something new. That has saved millions in duplicated effort, reduced delivery times, and made services easier to use. It’s one of the quiet success stories of modern government technology.

Changing the culture

What’s just as important as the code is the culture that came with it.

The GOV.UK Design System helped normalise open source inside government. GDS made it clear that openness is a strength vs a risk. Their mantra, “Make things open: it makes things better” is more than a tagline.

It created a new way of working - designing and coding in public, sharing research, and letting others build on your work.

The Ministry of Justice (MoJ) has taken this mantra to the next level. Their Digital and Technology team is one of the strongest adopters of open source in government. They built their own MoJ Design System, extending the GOV.UK patterns for justice-specific services like case management and prisoner booking. Even more impressive is Splink (great name). An open source Python package for ‘probabilistic record linkage’. Used across gov from health to defence. It’s serious backend engineering solving a real data problem.

The MoJ team doesn’t just share code; they share their learnings through their Justice Digital blog (built using their Design System frontend). I love it! Honestly, if I was looking for a job and wanted to work in Government I would be knocking on their door.

Other open source success stories

The culture of openness has spread.

• The Home Office has its own Design System for internal tools and services.

• The NHS created the NHS.UK Design System, now used across the whole health service.

• Local councils are collaborating through LocalGov Drupal, which lets them share code and design patterns instead of paying for separate CMS contracts.

There’s a pattern here. Shared infrastructure that saves time and money while improving quality. We’ve defined some great examples. Now we just need to push it further.

Where it could go next

Unsurprisingly, I’m going to talk about AI.

If open source made our web services more transparent and trustworthy, imagine what it could do for government AI systems. Models that can be inspected, pipelines that can be reused, and decisions that can be audited, all built on open foundations.

From my research (and I’d love to be proven wrong!) this isn’t happening yet and there aren’t any initiatives to make it happen. The best I could find was a £1m fellowship to “build open-source AI tools for public services,” but it seems more like a partnership to promote Meta’s Llama models than a genuinely open source programme.

This is the opportunity. Take what worked for digital services and apply it to AI. It would make public AI projects faster, safer, and more explainable. Reuse what works, make it open, and let the community improve it.

Where open source could make a real difference

Take the Department for Work and Pensions’ fraud-detection algorithm.

An investigation found that it disproportionately flagged people by age, disability and nationality. As Science and Technology Secretary Peter Kyle said, the public sector “hasn’t taken seriously enough the need to be transparent in the way that the government uses algorithms.”

In my view an open source, auditable model training pipeline would have helped massively. Our government obviously wants to deploy AI, but it should also publish how these systems are built, by whom, on what data, and with what fairness checks.

Transparency is one side of the problem, another is duplication.

The National Audit Office wrote an excellent report highlighting ‘overlap’ in the responsibilities for AI adoption between departments (DSIT and Cabinet Office). That’s a strong indicator that we’re duplicating effort at a policy and strategy level, let alone at a technical one!

I’d like to see a central policy that talks about building open source AI foundations that can be used across departments. The same principles that made GOV.UK a success could do the same for AI. It seems we’re a bit away from this, but it’s where we should be heading.

What’s holding us back

Despite the great examples from GDS and MoJ, many departments still buy proprietary software to solve the same kinds of problems multiple times.

Why? Partly culture. Partly procurement. Maybe a lack of confidence or awareness of how to run open projects long term. Maybe it’s a concern over open source being less secure than closed source (not an opinion I share!). The intention is usually good, to de-risk delivery, but it often leads to silos and wasted spend.

We should look to change the default and become more comfortable with working in the open. The success of projects from GDS and MoJ show that it’s entirely possible to build shared, production-grade software in public.

At Fuzzy Labs, we see opportunities to open source parts of our own AI projects in government. Because it makes things better. Open projects attract contributors, create transparency, and help build technical capability in our country.

And that’s really the point.

When we make things open, we don’t just make better software.

We make better teams, better learning, and maybe better pathways for the next generation looking up at those skyscrapers.

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And Finally…

Want to contribute?
If you’re working in MLOps or building open source AI tools, we’re always looking for guest writers. Share your production wins, your spectacular failures, or that tool you’ve built that actually solves a problem. The best insights come from people doing the work – so if you’ve got something worth sharing, get in touch.

While we’re on the topic, the team have been cooking something up…

We’ve been sketching out “recipes” for the MLOps lifecycle – reusable, step-by-step ways of solving common problems. Think handling new data safely, tracking experiments, serving models in production, setting guardrails, retrieval-augmented generation (RAG), and security.

It’s very exciting – we’ve even got personalised chef hats for every squad member. But the real question is this: which stage of the MLOps lifecycle do you think most needs a recipe right now? (Not rhetorical - answers encouraged.)

What’s Coming Up

📅 18th November: The MLOps.WTF meetup is back at Matillion for edition #6, where we’ll dig into monitoring and evaluating ML and agentic AI with real-world stories. Brad Smith (Spotted Zebra) will share how to build robust evaluation pipelines for GenAI, Daisy Doyle (Awaze) will talk lessons from fraud detection projects, and Julian Wiffen (Matillion) will introduce Maia, their GenAI-powered data engineer.

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About Fuzzy Labs

We’re Fuzzy Labs. A Manchester-rooted open-source MLOps consultancy, founded in 2019.

Helping organisations build and productionise AI systems they genuinely own: maximising flexibility, security, and licence-free control. We work as an extension of your team, bringing deep expertise in open-source tooling to co-design pipelines, automate model operations, and build bespoke solutions when off-the-shelf won’t cut it.

Currently: It’s an exciting time for us in Manchester, and as always, we’re calling out for great talent, see our open roles below:
MLOps Engineer
Senior MLOps Engineer
Lead MLOps Engineer

Liked this? Forward it to someone who’s interested in the potential of Open Source and how we can invest in our . Or give us a follow on LinkedIn to be part of the wider Fuzzy Labs community.

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