Can you help if we already have a model?

Yes. We can help assess, refine, productionize, or redesign an existing model workflow inside Fabric.

Do you only build net-new models?

No. Some engagements begin from scratch, while others focus on operationalizing or improving work that already exists.

Will our internal team be able to support the model after the engagement?

That is the goal. We build with transparency, documentation, and operational handoff in mind from the start.

Is this only for advanced ML teams?

No. This service supports both organizations launching their first production model and teams scaling existing machine learning work.

How long does a typical engagement take?

That depends on scope and your current environment. The free Production Readiness Assessment is the right starting point — we will give you a realistic timeline estimate after that conversation.

Production-ready ML models built inside your Fabric

ML model development for Microsoft Fabric is the end-to-end process of designing, training, validating, and deploying machine learning models that run natively inside a Microsoft Fabric environment.

Lunar Point Systems builds custom ML models for enterprise data teams that need something production-ready, not another prototype sitting in a notebook.

Built natively inside Microsoft Fabric

Aligned to enterprise governance and security standards from the start

Designed for your team to own and operate independently

Work with a trusted Microsoft certified team

Get a Free Production Readiness Assessment

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Many teams can build a model in a notebook. Few can turn that work into a dependable production system.

Even less can build a model that fits the broader data platform, meets governance requirements, and can be operated by the internal team without external support. That is the gap Lunar Point Systems' ML Model Development service is built to close.

Launch a new predictive use case inside Microsoft Fabric

Move a prototype into a governed, production-ready deployment

Replace manual scoring or spreadsheet-driven decision workflows with real ML

Improve reliability and transparency for an existing model workflow

Designed for predictability,
not surprises

Every model starts with a clear understanding of your business objective, data environment, and how the model will operate inside Fabric. We define the architecture, feature requirements, and success criteria before training begins, so there are no surprises at deployment.

View Our Engagement Models

See How We Work

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The practical difference for your team.

No Separate Vendor Assessment

Because we operate inside your environment, your security team doesn't need to onboard or assess us as an external vendor. We fall within the perimeter they already manage.

Fits Regulated Industries

Healthcare, finance, and government teams can engage confidently. No data leaves your environment, so existing data transfer restrictions simply don't apply.

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Faster Time to Value

No new vendor onboarding, no procurement cycles, no integration work. We operate inside the stack you already own and can start immediately.

How We Work

Full Control Stays With You

You define the access, own every output, and can revoke permissions at any point. Nothing we build or touch leaves your control.

See How to Engage

Everything you need to know

How this service works, what’s included, and what to expect from an engagement. If yours isn’t answered here, the fastest path is a short conversation.

Build, Deploy, and Operate Machine Learning in Microsoft Fabric

We help organizations move machine learning from experimentation to reliable production by designing and implementing Fabric-native ML systems that align with enterprise data, security, and governance standards.

Whether you’re building your first production model or scaling an existing ML footprint, we work inside your Fabric environment to ensure models are owned, operated, and evolved confidently by your teams.

Start a conversation about your Fabric roadmap

Book a Call