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Generative AI & Copilot / SUB-PRACTICE 3.5

Machine Learning & MLOps.

Every Machine Learning & MLOps service AI Solution Technologies delivers — 2 in total, within the Generative AI & Copilot practice.

Book a scoping call →See how it runs
End-to-end machine learning delivery from dataModel registry, lineage and reproducibilityAutomated retraining and release pipelinesDrift and fairness monitoring in production
BUILT ON
MicrosoftAzureAWSGoogle CloudDatabricksSnowflake

The problems Machine Learning & MLOps exists to solve.

Forecasts produced by spreadsheet and judgement alone
Models built once and never monitored after deployment
No repeatable path from experiment to production
Model performance degrading silently as conditions change

What is Machine Learning & MLOps?

SENIOR-LEDFeature engineering and model development
PRODUCTION-READYModel evaluation against agreed accuracy and business metrics
KPI-DEFINEDProduction deployment pipeline with versioning

Get a free discovery sprint

Before you commit, we help you confirm the use case, check data readiness, define the KPI, and work out which of the 2 Machine Learning & MLOps services actually fit the problem you have.

For a limited number of teams each quarter, we run the full sprint free of charge.

Claim a free sprint →
THIS SUB-PRACTICE
2SERVICES
0NEW FOR 2026-27
7STAGE DELIVERY

How Machine Learning & MLOps runs.

01
Confirm objectives, stakeholders, constraints and success measures
Review current systems, data, controls and delivery readiness
Prepare the target design, backlog, governance and implementation plan
Test the priority requirements through a prototype, pilot or controlled design review
Configure, integrate, test and deploy the approved solution
Train users, transfer knowledge and establish operating procedures
Monitor adoption, performance, cost, quality and improvement opportunities
KEY CAPABILITIES

What we bring to Machine Learning & MLOps.

  1. 01End-to-end machine learning delivery from data to production
  2. 02Model registry, lineage and reproducibility
  3. 03Automated retraining and release pipelines
  4. 04Drift and fairness monitoring in production
  5. 05Integration with the client's existing data platform

What you get, and what follows

DELIVERABLESFeature engineering and model developmentModel evaluation against agreed accuracy and business metricsProduction deployment pipeline with versioningMonitoring for drift, data quality and performanceRetraining strategy and triggersDocumentation and handover
BUSINESS OUTCOMESForecasts and predictions with measured accuracyModels that survive contact with productionEarly detection of degradationA repeatable pathway for the next modelReduced reliance on individual analysts

The 2 services inside Machine Learning & MLOps.

2 SERVICES
Machine Learning SolutionsDevelopment of predictive and classification models for forecasting, risk scoring, segmentation and optimisation.MLOps & AI InfrastructureThe pipelines, registries, environments and release processes that take models from notebook to monitored production.
INVESTMENT & DELIVERY

How we engage on Machine Learning & MLOps.

INDICATIVE VALUE
A$250,000 – A$1,200,000
The bands across these 2 services. Each service page carries its own; the scoping call confirms the range for your environment before anything is committed.
DELIVERY MODELS
Milestone based, or time and materials for evolving scope
Delivered by a senior-led Australian team across 7 stages, from discovery through to optimisation.Discuss scope and timing ›
WHO THIS IS FOR
  • Banks, insurers, superannuation funds and professional-services firms
  • Retail, property and logistics operators
  • Utilities, energy and resources operators
  • Large enterprises and mid-market organisations undergoing modernisation

What our customers say.

All customer stories
"Reconciliation that took our team nine days now closes in three — with a full audit trail on every match. It changed how the board sees AI.
Finance ManagerGCC Construction Group, Dubai
"Site teams stopped digging through folders. They ask the assistant, they get the clause with a citation, and they move on.
Project DirectorTier-One Contractor, Sydney
"The anomaly models flag outliers the week they appear, not at quarter-end. We stopped two overruns before they hit the P&L.
Commercial DirectorProperty Developer, Dubai
"Seven subsidiaries of spreadsheets became one governed reporting platform. The executive finally trusts the numbers on the screen.
Group CFOInfrastructure Holding, Riyadh

Our partnerships with
industry leaders

MicrosoftSolutions Partner
Google CloudPartner
SalesforcePARTNER
AWSpartner
network

Elsewhere in Generative AI & Copilot.

The whole practice →
SUB-PRACTICE 3.1Enterprise AI Assistants6 servicesExplore ›SUB-PRACTICE 3.2Microsoft Copilot17 services · 16 newExplore ›SUB-PRACTICE 3.3GenAI Engineering20 services · 20 newExplore ›SUB-PRACTICE 3.4Grounding, Safety & Evaluation8 services · 8 newExplore ›

Scope Machine Learning & MLOps in one call.

Tell us the problem. We'll tell you which of the 2 services fit — and exactly how we'd deliver them.

Book a scoping call →
← Back to all Generative AI & Copilot services