Build Machine Learning the right way inside Microsoft Fabric.

We implement the workflow infrastructure that machine learning needs to operate reliably at scale inside Microsoft Fabric. Feature engineering pipelines, inference patterns, retraining cadences, and governance alignment all built for your enterprise data environment.

Fabric ML enablement is the implementation of the underlying workflow infrastructure that allows machine learning to operate reliably and repeatably inside Microsoft Fabric. It covers feature engineering pipelines, inference and retraining workflows, model versioning, data lineage, and governance alignment.

Lunar Point Systems helps enterprise data teams establish this foundation before scaling ML introduces inconsistency and technical debt.

Fabric-native workflow design aligned to how Fabric is meant to operate
Built for repeatability, governance, and internal team ownership
Reduces technical debt before scale creates problems
Work with a trusted Microsoft certified team
Talk to a Fabric ML Specialist
Talk to a Fabric ML Specialist
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Build your foundation the right way inside Fabric before scale makes it expensive to fix.

Machine learning becomes difficult to scale when feature engineering, inference, retraining, and governance evolve inconsistently across teams or use cases. The underlying workflow infrastructure matters as much as the model itself.

Establish consistent, repeatable ML workflow patterns inside Microsoft Fabric
Standardize feature engineering so training and inference use the same logic
Reduce technical debt created by ad-hoc notebook-based ML workflows
Align machine learning operations to enterprise governance and data standards

Designed for predictability, 
not surprises

We help organizations implement machine learning in ways that align with how Microsoft Fabric is designed to operate. That means less disconnected tooling, stronger governance, clearer workflows, and a more sustainable path to production use across the long term.

View Our Engagement Models
View Our Engagement Models
See How We Work
See How We Work
Feature Engineering Pipelines

Reusable feature engineering pipelines embedded in Fabric that ensure consistent data preparation across training and inference workflows, eliminating training-serving skew.

Inference & Retraining Workflows

End-to-end inference and retraining workflows built to run reliably in production, with scheduling, orchestration, and failure handling designed for enterprise environments.

Versioning & Lineage Patterns

Repeatable patterns for tracking model versions, data lineage, and workflow changes across your Fabric environment. Built to meet enterprise source control and governance standards so every change is traceable and every decision is auditable.

Governance Alignment

ML workflows aligned to your existing data architecture, security policies, and governance standards to ensure consistency with the broader data strategy already in place.

Documentation for Internal Teams

Operational documentation covering workflow design, configuration, and extension so your teams can maintain and evolve the ML infrastructure independently.

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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.
See Our Services
See Our Services
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
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
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.

What is Fabric ML Enablement?

Fabric ML Enablement is the work of building the workflow infrastructure that machine learning needs to run reliably inside Microsoft Fabric. Feature engineering pipelines, inference workflows, retraining cadences, governance patterns, and versioning controls, all built for enterprise teams that need ML to operate as a sustainable platform capability, not just a collection of notebooks.

How is this different from ML model development?

Model development builds the model. Fabric ML Enablement builds the infrastructure around it. Feature pipelines, inference workflows, retraining processes, and governance alignment. The two services are designed to work together and often do.

Do we need this if we already have data pipelines?

Possibly. Existing data pipelines may not fully support ML-specific requirements like feature consistency between training and inference, retraining orchestration, model lineage, and governance-aligned versioning. Fabric ML Enablement addresses those gaps specifically.

Is this useful before we scale ML?

Yes — and it is most valuable before scale. Implementing consistent workflow patterns early reduces the technical debt that accumulates when ML work grows without a governed foundation.

Can you work with our internal team?

Yes. This service works well as a collaborative engagement with your internal data, analytics, and platform teams. Advisory + Pairing is specifically designed for organizations that want to build internal capability alongside the implementation.

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
Book a Call
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Lunar Point Systems designs and deploys production machine learning systems that operate natively within Microsoft Fabric environments.

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