Bringing MLOps Frameworks to Government (GCC) 

I had the pleasure of leading a comprehensive webinar recently on MLOps frameworks (machine learning operations) for Microsoft Government Community Cloud (GCC) organizations. 

As someone who's spent the last six years in the Microsoft data and AI space, I've seen firsthand how critical it is for government agencies to modernize their MLOps. 

Here are some key insights from our discussion. 

The Collaboration That Started It All 

This webinar came together through our on-going collaboration with the Microsoft State and Local Government team for Oregon and Idaho. What started as an idea we'd been floating around internally for some time really gained momentum thanks to their partnership. It's always energizing when you can combine internal expertise with strong partnerships to address real market needs. 

The Universal Challenge: Breaking Down Silos 

One thing I want to emphasize is that the challenges we discussed aren't just unique to government, though they're certainly amplified in that space. Whether we're talking about enterprise or government organizations, we consistently see the same problematic pattern: developers working in isolation, building custom solutions without adequate collaboration, visibility, or transparency. 

I often use a car manufacturing analogy to illustrate this point. Right now, many organizations have incredibly talented "mechanics" building amazing custom "cars" in their garages. The end product might be impressive, but the process isn't scalable, production-ready, or transparent enough to build organizational trust.  

What we need is a production line approach to building ML models that maintains quality while enabling scale and visibility. 

The ML ROI Crisis: Why 2025 is Different 

Let me be frank about something that's driving a lot of the urgency around MLOps right now. After experiencing a lot of general failed AI project experiences in 2024, there's a big focus in 2025 on demonstrating clear ROI. This is a major reason why we've seen a resurgence in machine learning projects, and a pivot from the hype cycle of generative-AI focused projects to more battle-tested AI use cases. 

When we're presenting why we're going down these developmental paths, we need to make a compelling ROI case. And the statistics are pretty sobering: More than half of AI models never make it to production. This isn't a figure I've made up; this comes from Gartner research, and it reflects the reality of data scientists working in silos with lack of project governance and real visibility into what's being developed. 

The Real Cost of Siloed Development in Government 

During the webinar, we discussed some specific problems we see when government agencies operate MLOps in silos, and they're more costly than many organizations realize: 

1. Constant Reworking and Rebuilding: Without an MLOps framework that supports agency-wide visibility and collaboration, teams often end up solving the same problems in isolation. A data scientist might spend months developing a high-performing model, but without shared infrastructure, documentation, or standardized processes, that work remains siloed. When they move on, the next team starts from scratch instead of building on proven assets already available in the feature store. 
2. Models That Miss Their Window: When models are developed in isolation with limited cross-team collaboration, the process often takes so long that the original business need has changed. Priorities may shift, requirements evolve, or another team may have already implemented a solution. 
3. Lack of Reproducibility: Without proper frameworks, agencies can't consistently reproduce results. This becomes a major issue when leadership or auditors ask, "How did you arrive at this conclusion?" and there's limited lineage to demonstrate the methodology. 
4. No Reusability of Feature Engineering: Some of our most talented data scientists do incredible work in feature engineering, but in siloed environments, that hard work gets buried. Other teams don't even know it exists, let alone how to leverage it for their own projects. 
5. Inability to Stand Behind Models: Perhaps most critically, agency leaders can't confidently stand behind models when there's insufficient visibility into how they were built. When you're making decisions that affect public services or policy, you need to be able to defend your methodology with complete transparency. 

Why MLOps Frameworks Matter for Government 

From a compliance perspective, transparency is absolutely critical in government environments. Agencies need to understand exactly what's happening behind the scenes, and how models are being produced, deployed, and maintained. Our MLOps framework provides that scalable production line approach while ensuring the visibility and governance that government organizations require. 

What MLOps Frameworks Provide 

During the webinar, I outlined the core pillars that our MLOps framework delivers, each addressing specific challenges government organizations face: 

1. Visibility and Reproducibility

This is about ensuring ML models are built in a common framework that's shared across multiple environments – not just isolated on local machines. We implement: 

• Structured versioning and model lineage tracking: You can see exactly how models evolved over time 

• Clear documentation standards: Every decision and methodology is documented for future reference 

• Transparency that builds trust: Leadership can understand and defend the work being done 

The goal is creating an environment where you can confidently present your work to constituents and oversight bodies because you have complete visibility into the development process. 

2. Continuous Integration and Deployment (CI/CD)

This pillar bridges the gap between the experimental nature of data science and the disciplined processes needed for production environments. We automate: 

• Testing and approval processes: Models go through rigorous validation before deployment 

• Deployment workflows: Streamlined processes that maintain quality while accelerating time-to-production 

• Performance monitoring: Continuous oversight once models are live 

The result is faster deployment cycles without sacrificing the quality and performance standards government agencies require. 

3. Scalable Modeling Framework

This is where we really address the collaboration challenge. Our framework provides: 

• Platform-agnostic foundation: We've deployed this across Azure ML, Databricks, and Fabric 

• Seamless onboarding for new teams: Standardized processes that new team members can quickly adopt 

• Reusable feature engineering: Teams can build upon previous work instead of starting from scratch 

• Evolutionary development: Models improve incrementally rather than being rebuilt completely 

The Speed Factor: 10X Faster Deployment 

Here's where the ROI case becomes really compelling. When we set up this framework for clients from the beginning, we create what we call "a push of a button" deployment process. Development environments are established, where teams can click deploy and push everything into production with built-in validations to ensure there are no conflicts with existing production code. 

This automated approach can speed up delivery time by factors of 10 compared to manual deployment processes. Instead of having to manually reconfigure and refactor models for different environments after they're built, everything is automatically configured for seamless transition from development to production. 

The 10X improvement figure is not exaggerated. It’s based on Gartner research that highlights the significant efficiency gains organizations achieve when they move from manual to automated deployment processes. For government agencies operating under tight budgets and timelines, this kind of efficiency improvement represents massive cost savings and faster delivery of public services. 

MLOps Frameworks in Action: Collaboration at Scale 

What we really want to create with an MLOps framework is the ability to collaborate at scale. This means: 

• Trust in the work: Clear lineage and documentation that builds confidence 

• Continuous improvements: Track records that show how models evolve and improve 

• Production focus: Getting models out of development and into environments where they drive real value 

I've seen too many brilliant models that never make it past the experimental phase because there's no clear path to production. Our framework eliminates that bottleneck. 

Our Approach: Discovery Before Development 

One of the most important aspects of our framework is starting with comprehensive discovery sessions. I've seen too many projects jump straight into development without properly understanding the existing landscape, compliance requirements, and realistic resource constraints. These framing sessions often lead to significant shifts in project scope and priorities once organizations truly understand the costs and effort associated with different approaches. 

We focus on assessing existing workflows and models that might already be in production, identifying what can be reused within the new framework, and determining the optimal platform for each agency's specific needs.  

While Azure Databricks is typically our preferred platform for GCC environments, we always tailor our recommendations based on agency requirements and compliance considerations. 

The Fresche Solutions Advantage 

Since OmniData's acquisition by Fresche Solutions in January, we've maintained our Microsoft focus while gaining access to a broader team and expanded capabilities. As a Microsoft solutions partner, we bring deep expertise in three core areas: advanced analytics and AI, data monetization, and cloud modernization. Our regional presence across California, Washington, Texas, and internationally in the Netherlands and UK allows us to support government organizations wherever they're located. 

Looking Forward 

The enthusiasm and engagement we saw during the webinar reinforced my belief that government organizations are ready for this modernization. They understand the challenges of siloed development and recognize the value of implementing proper MLOps frameworks. The key is partnering with organizations that understand both the technical requirements and the unique compliance and governance needs of the government space. 

If you're interested in learning more about how MLOps can transform your organization's machine learning operations, or if you missed the webinar and want to explore these concepts further, you can check out the recording here. We also have a PDF you can download here that breaks down the framework if you’d like to send this to someone else on your team, or get in front of your internal leadership for consideration.

The future of government AI and machine learning depends on building these scalable, transparent, and collaborative frameworks – and there's never been a better time to get started.