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Machine Learning 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.

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.

How teams typically engage

Engagement Models

Project-Based

A focused engagement to design, train, and deploy a purpose-built machine learning model, with clearly defined scope, outcomes, and delivery timelines.

Build + Monitor

An ongoing engagement providing continued support after deployment, including model performance monitoring, retraining patterns, and lifecycle management to ensure reliability as data and requirements evolve.

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Fabric ML Enablement

Building Machine Learning the right way in Microsoft Fabric

We implement machine learning pipelines that follow Fabric’s native design paradigms. This includes feature engineering, versioning, retraining rules, and inference workflows aligned to enterprise data estates and operating standards.

Get Starterd

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

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.

How teams typically engage

Explore Engagement Options

Project-Based

A defined engagement to implement Fabric-native ML workflows and foundational patterns aligned to your data architecture and operating environment.

Advisory + Pairing

Hands-on collaboration with your teams to guide implementation while establishing long-term ownership and sustainable internal capability.

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Model Monitoring & Lifecycle Management

Machine learning built for lasting trust.

Continuous monitoring and lifecycle processes to ensure models remain accurate, reliable, explainable, and aligned to business KPIs after deployment.

Get Starterd

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.

How teams typically engage

Talk Through Monitoring Options

Build + Monitor

An ongoing engagement focused on production monitoring, drift detection, retraining cadence, and long-term model health.

Post-deployment support

Targeted support following an initial model build or Fabric implementation to ensure stability, confidence, and smooth operational handoff.

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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.

Get Starterd

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

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.

How teams typically engage

Engagement Models

Advisory + Pairing

For teams that want senior architectural guidance while keeping implementation in-house. We work alongside technical leads to review designs, validate decisions, and shape a clear, defensible path forward.

Project-Based

A focused engagement delivering a defined assessment, architectural findings, dependency mappings, and prioritized next steps to support confident 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

Machine Learning Services ‍Built for Microsoft Fabric

Purpose-Built Machine Learning Models for Production

How teams typically engage

Building Machine Learning the right way in Microsoft Fabric

How teams typically engage

Machine learning built for lasting trust.

How teams typically engage

Get the architecture right before you build.

How teams typically engage

Build, Deploy, and Operate Machine Learning in Microsoft Fabric

Machine Learning Services
‍Built for Microsoft Fabric