AI & Machine Learning · 5 of 5

Model Ops & Governance

Implement MLOps best practices for model deployment, monitoring, versioning, and governance to ensure reliable AI operations.

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Core technologies
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Years of engineering
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Projects delivered

Why it matters

Where Model Ops & Governance fits

Implement MLOps best practices for model deployment, monitoring, versioning, and governance to ensure reliable AI operations.

This sits inside our ai & machine learning practice, and rarely arrives alone — most engagements combine it with two or three of its neighbours. The assessment decides which, and in what order.

What the practice is measured on

  • Accelerated Innovation

    Deploy AI solutions 3x faster with our proven frameworks

  • Cost Optimization

    Reduce operational costs by up to 40% through automation

  • Enhanced Experience

    Improve customer satisfaction with personalized AI interactions

  • Data-Driven Insights

    Make better decisions with predictive analytics

Generative AI Solutions

Azure OpenAI, GPT-4, Copilot Studio, RAG Architecture, LangChain

Predictive Analytics

Azure ML, Python, TensorFlow, PyTorch, Scikit-learn

Computer Vision & NLP

Azure Cognitive Services, OpenCV, BERT, Transformers, OCR

  • MLMLflow
  • Azure DevOps
  • MRModel Registry
  • ABA/B Testing
  • MOMonitoring
  • Microsoft Azure
  • Amazon AWS
MO

The Tech Stack We Work In

The tooling we actually build model ops & governance on.

  • Google Cloud
  • Azure OpenAI
  • Databricks
  • Snowflake
  • Kubernetes
  • Docker
  • Terraform

AI Integration Platforms

Power Platform, AI Builder, M365 Copilot, Custom Copilots, API Integration

Model Ops & Governance

MLflow, Azure DevOps, Model Registry, A/B Testing, Monitoring

You are here

How We Deliver It

A sequence you can plan around, with a decision point at the end of each phase rather than one big reveal at the end.

01

Discovery & Assessment

Analyze your business needs and identify AI opportunities

02

Strategy & Planning

Design AI architecture and implementation roadmap

03

Development & Training

Build and train custom AI models for your use cases

04

Integration & Deployment

Deploy AI solutions into your production environment

05

Monitoring & Optimization

Continuously monitor and improve model performance

Why organisations choose us for this

Five commitments that hold on every engagement, not just the ones that go well.

01

Audit first, build second

Every engagement opens with an assessment that produces a prioritised backlog. Engineering starts against that, not against an assumption.

02

Incremental, not big bang

Existing systems keep running while we work. Delivery arrives in increments you can put in front of users rather than one release at the end.

03

Fixed scope per phase

Each phase has defined outputs and a defined cost, with a decision point at the end. You can stop between phases without stranding the work.

04

Ownership-first documentation

Architecture decisions are written down with their rationale, in your repositories, so the reasoning survives the people who made it.

05

Compliance-aware by default

Access control, auditability, and data residency are settled in the first architecture review rather than retrofitted before an audit.

Frequently Asked Questions

What we are asked most often about model ops & governance.

Almost never. We work incrementally around what you already run, extracting interfaces and migrating in phases so the existing system keeps serving users while the new one takes over piece by piece.

That is what the assessment establishes. We map your current architecture, data and constraints first, and if the approach will not hold in your environment we say so before anyone commits to a build.

Discovery is fixed-price and ends with a costed roadmap. Build phases are then priced per phase against defined outputs, so you are never approving an open-ended budget.

You do — code, infrastructure definitions, any trained models, and the documentation. All of it lands in your own repositories and cloud tenancy as we go.

Monitoring, alerting and agreed response targets are part of delivery. Where we also run the platform under managed services, we are the ones on the other end of the alert.

Get a free model ops & governance assessment

We map what you have, what it would take, and in what order — specific to model ops & governance in your environment.

Book a Free Architecture Review

What you get from the audit

  • A complete architecture map of what you have today
  • A prioritised change list, worst-first
  • A sequenced roadmap broken into phases
  • A fixed-price estimate for phase one

Yours to keep whether or not you engage us.

Let's Work on Your Next Project.

Salesforce Certified
Microsoft MVP
MuleSoft Certified
AWS Certified
Kubernetes Certified
Azure DevOps