Machine Learning Services
‍Built for Microsoft Fabric

Whether establishing ML capabilities in Fabric or bringing existing models into production, we meet teams where they are and deliver systems designed for long-term operation and internal ownership.

Book a Free Production Readiness Assessment
Book a Free Production Readiness Assessment
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ML Model Development

Purpose-Built Machine Learning Models for Production

We design and deploy purpose-built machine learning models tailored to your data, workflows, and business objectives. Each model is engineered from the ground up, never templated or reused, ensuring seamless production integration and measurable impact from day one.

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Build My Custom Model
Build My Custom Model
Accelerate predictive insights while retaining full operational control
Deliver models aligned to established Fabric data structures and tooling
Minimize vendor dependency and maintain full internal ownership of models and pipelines
Production-ready machine learning models
Expertly trained and validated, with performance, reliability, and maintainability from the start.
Fully operational Fabric workflows for inference and retraining
Deployment-ready pipelines supporting scoring, retraining, and end-to-end lifecycle management.
Quantified performance results and model evaluation reports
Clear metrics and evaluation outputs for real-time performance monitoring and decision-making.
Internal-ready documentation and handoff materials
Comprehensive documentation enabling internal teams to operate, monitor, and extend models independently.
Fabric ML Enablement

Building Machine Learning the right way in Microsoft Fabric

Fabric ML enablement is the implementation of the workflow infrastructure: feature engineering pipelines, inference patterns, retraining cadences, and governance alignment. This allows machine learning to operate reliably inside Microsoft Fabric at scale. Lunar Point Systems helps organizations establish this foundation before scaling ML creates technical debt.

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Talk to a Fabric ML Specialist
Talk to a Fabric ML Specialist
Minimize technical debt by leveraging built-in Fabric patterns
Create repeatable, governable ML processes that internal teams can operate and maintain
Avoid common implementation pitfalls that slow adoption and scale
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Feature engineering pipelines
Reusable feature engineering pipelines embedded in Fabric to ensure consistent data preparation across training and inference workflows.
Inference & retraining workflows
End-to-end machine learning workflows that run reliably in production and support ongoing inference, retraining, and model improvement.
Governance, versioning and lineage
Clear, repeatable patterns for tracking model versions, data lineage, and changes over time, aligned with enterprise governance and source control practices.
Compliance with your data strategy
Machine learning workflows aligned to your existing data architecture, security policies, and governance standards to ensure consistency with broader data strategy.

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
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Model Monitoring & Lifecycle Management

Machine learning built for lasting trust.

Model  monitoring and lifecycle management is the ongoing process of detecting drift, tracking performance against business KPIs, managing retraining  cadences, and maintaining governed model versioning after a machine learning  model goes into production. Lunar Point Systems implements these processes natively inside Microsoft Fabric environments.

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Get a Free Monitoring Gap Review
Get a Free Monitoring Gap Review
Protect production quality by detecting data drift, model drift, and performance degradation early
Formalize retraining cadence and alerting strategies to support controlled, auditable updates
Enable operational ownership so your teams can confidently manage models in production
Performance and drift monitoring
Ongoing tracking of model performance, data drift, and prediction drift using metrics tied directly to business impact, not just model accuracy.
Retraining cadence and governance patterns
Defined retraining schedules, approval checkpoints, and versioning practices that support controlled updates and lifecycle consistency.
Alert workflows and SLA alignment
Monitoring alerts and escalation paths designed to integrate with existing operational processes, including Fabric-native alerting and enterprise support workflows.
Ongoing monitoring and post-deployment support
Structured monitoring engagements to review performance, address emerging issues, and support ongoing model evolution.
Architecture & Implementation Guidance

Get the architecture right before you build.

Advisory engagements designed to integrate machine learning into your existing data and analytics ecosystem with minimal disruption and strong alignment to governance, security, and operating standards.

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Request an Architecture Review
Request an Architecture Review
De-risk architectural decisions early in the ML adoption lifecycle
Validate current-state readiness against enterprise data, security, and governance standards
Ensure scalable, maintainable implementations that can grow with business demand
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Architecture reviews and recommendations
A structured assessment of your current data and analytics architecture, with clear recommendations for integrating machine learning within Microsoft Fabric using proven patterns.
Data flow and dependency mapping
Detailed mapping of upstream data sources, transformations, and downstream consumers to surface operational risks, ownership boundaries, and scaling constraints early.
Guided implementation workshops
Collaborative working sessions with your technical teams to walk through recommended architectures, validate assumptions, and align on implementation decisions before build-out.
Deployment and scaling best practices
Practical guidance for deploying machine learning workloads in Fabric and scaling them safely as usage, complexity, and operational expectations grow.
Applied AI & Copilot Enablement

Practical, governed AI built around real business workflows.

We help organizations design and operationalize applied AI solutions, internal assistants, copilots, and grounded knowledge workflows, that align with Microsoft Fabric and enterprise governance requirements. The goal is not AI for novelty. It is useful, trusted AI that supports real business workflows and can be owned and operated confidently by internal teams.

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Discuss Your AI Use Case
Discuss Your AI Use Case
Identify AI opportunities grounded in realistic, high-value business use cases
Design AI workflows connected to trusted internal knowledge and governed data sources
Establish evaluation, guardrails, and governance so quality and risk are managed intentionally
AI use-case prioritization and framing
We identify which AI opportunities are worth pursuing based on your data environment, workflows, and governance requirements — so effort goes toward use cases that are practical, high-value, and ready to build.
Copilot and assistant workflow design
We design the end-to-end workflow for your internal assistant or copilot — including retrieval patterns, grounding against trusted internal data sources, and integration with your existing Fabric environment.
Guardrail and governance framework
We define the evaluation criteria, access controls, and operational guardrails your AI system needs to be trusted and managed responsibly — built into the design from the start, not retrofitted later.
Documentation and internal handoff
We deliver structured documentation and handoff materials so your team understands how the system works, how to evaluate its outputs, and how to extend or update it without external dependency.

What our Microsoft partnership and certification means in practice.

Platform-Native Expertise

Aligned with Microsoft's roadmap, built natively inside Fabric and Azure.

Verified Technical Commitment

Formally recognized by Microsoft for meeting enterprise data and security standards.

Access to Platform Resources

Direct access to Microsoft's technical teams and documentation, so issues get resolved faster.

Fit for Your Existing Stack

Solutions slot into existing Microsoft 365, Azure, or Fabric environments without new vendors.

No Additional Vendor Risk

Operates inside your environment, no third-party data transfers or new attack surfaces.

Long-Term Reliability

Microsoft Fabric is here to stay. Our partnership keeps us close to the roadmap. So your ML investment stays future-proof.

Common questions

How we work, what our services include, and what to expect from an engagement. If you have a question that isn't answered here, the fastest path is a short conversation.

What services does Lunar Point Systems offer?

Lunar Point Systems offers five services purpose-built for Microsoft Fabric environments: ML model development, Fabric ML enablement, model monitoring and lifecycle management, architecture and implementation guidance, and applied AI and copilot enablement. All services are designed for enterprise teams building and operating machine learning inside Fabric.

Do you work exclusively with Microsoft Fabric?

Yes. Lunar Point Systems works exclusively with Microsoft Fabric. Every service — from ML model development to applied AI enablement — is designed around Fabric’s native architecture, governance capabilities, and enterprise operating standards.

How do you handle model monitoring after deployment?

We implement continuous drift detection, performance monitoring tied to business KPIs, and defined retraining cadences. We also build alert workflows and escalation paths that integrate with your existing operational processes — not just model-level accuracy metrics.

Do you build every model from scratch?

Yes. Every ML model Lunar Point Systems builds is engineered from scratch for the client’s specific data, workflows, and business objectives inside their Fabric environment. Models are never templated or reused across engagements.

What is applied AI, and how is it different from ML model development?

ML model development focuses on predictive models trained on your data. Applied AI enablement focuses on AI-driven user experiences and workflow support — such as internal assistants, copilots, and knowledge tools — grounded in enterprise information and business context.

Can applied AI be governed like the rest of our platform?

Yes — and it should be. Governance, data access controls, evaluation, and operational ownership should all be considered from the beginning, not added later.

What engagement models are available?

Engagements are structured as project-based builds, ongoing Build + Monitor retainers, or Advisory + Pairing collaborations. The right model depends on your team's goals, internal capability, and where you are in the ML or AI lifecycle.

What does internal ownership mean in practice?

It means your teams can operate, monitor, retrain, and extend models and AI systems without ongoing dependency on us. We deliver full documentation, structured handoff materials, and — where applicable — hands-on pairing to build internal capability alongside the system itself.

What does Lunar Point Systems do?

Lunar Point Systems is a Microsoft Fabric-specialist firm that designs and deploys production machine learning systems and applied AI workflows for enterprise data teams. Services include ML model development, Fabric ML enablement, model monitoring and lifecycle management, architecture and implementation guidance, and applied AI and copilot enablement. All work is delivered natively inside Microsoft Fabric and built for long-term internal ownership.

How is Lunar Point Systems different from a general ML consulting firm?

Lunar Point Systems works exclusively with Microsoft Fabric, which means every engagement is built around Fabric’s native architecture, governance capabilities, and enterprise data standards. Unlike general ML consultancies, LPS designs for internal ownership from the start — every model, pipeline, and AI workflow is documented and handed off so your team operates it without ongoing external dependency.

Your data's ready to do more than report.

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.

Book a Fabric Review and leave with a clear read on where your environment stands and
the highest-value model to build first. No pitch. You keep the findings.

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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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