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Build prediction workflows your team can evaluate.
Use available data to assess a defined prediction or classification task. Establish a baseline, review data quality and decide whether a model improves the workflow enough to justify operating it.
★★★★★5.0· 57 client reviews
Overview
Machine Learning Development, explained
Machine learning finds patterns in historical data to make predictions, such as which customers may churn, how much stock to order or which transactions look unusual. It works best on decisions that repeat often and have enough past examples to learn from.
We start by establishing a simple baseline and checking whether your data can beat it. If it can, we build the model, the data pipeline that feeds it and the integration that puts predictions where people make decisions, then monitor accuracy as data changes over time.
Before you build
Decisions worth settling first
Settling these points early keeps scope realistic and makes the estimate meaningful.
01
Decision to improve
State the decision, how it is made today and the cost of getting it wrong.
02
Historical data
Confirm the data available, its time range, quality and labels.
03
Baseline
Measure current performance or a simple rule so improvement is clear.
04
Success metric
Choose metrics that reflect business value, not just model accuracy.
05
Where predictions are used
Decide how results reach users or systems and how often.
06
Monitoring plan
Plan how to detect drift and when to retrain.
Services
What our machine learning development services include
Predictive analytics
Forecasting demand, revenue, churn or risk from historical data.
Recommendation systems
Personalised product, content or next-best-action recommendations.
Classification models
Automatic categorisation of records, transactions or support requests.
Anomaly detection
Identification of unusual transactions, sensor readings or behaviour.
Data pipelines
Collection, cleaning and feature engineering pipelines that keep models fed.
MLOps
Model deployment, versioning, monitoring and retraining workflows.
How it is built
The layers behind a dependable result
Every layer has an owner, tests and documentation, so the system can be supported after launch.
01
Data preparation
Collection, cleaning, labelling and feature engineering.
02
Modelling
Training and comparing models against the baseline with fair evaluation.
03
Deployment
APIs or batch jobs that deliver predictions to applications.
04
Monitoring
Tracking accuracy, drift and business impact with retraining triggers.
Use cases
Where it helps
Retail and ecommerce
Demand forecasting and product recommendations.
Financial services
Risk scoring and transaction anomaly detection.
Logistics
Delivery time estimates and capacity planning.
SaaS products
Churn prediction and usage-based insights.
Manufacturing
Predictive maintenance from sensor data.
Health and wellness apps
Personalised plans and progress predictions.
Illustrative use case
An operations team can compare a forecasting model with its existing planning baseline before deciding how to use predictions.
Why PoliteTech
Why teams choose PoliteTech
Scope before commitment
Priorities, assumptions and acceptance criteria are agreed in writing before a delivery plan is fixed.
Engineers who build
Recommendations come from the people who design, code and support the software, so they reflect real implementation effort.
Visible progress
A shared backlog, regular demos and recorded decisions keep you informed without chasing updates.
Practical AI
Evaluation sets, review steps, cost limits and monitoring are part of every AI feature we ship.
Flexible engagement
Start with a scoped project, a dedicated team or support, and change the model as your roadmap evolves.
Clear ownership
Repositories and accounts can live in your organisation, with IP and confidentiality terms agreed in the contract.
Industries
Where this work fits
Retail and Ecommerce
Build web and mobile journeys around product discovery, ordering and customer service.
Financial Software
Support reporting, document processing and internal operations through defined application workflows.
Logistics Software
Connect planning, task assignment and operational updates in one workflow.
Education and Elearning
Create platforms for content, activities and progress tracking.
Fitness and Wellness Apps
Support tracking, coaching content and user engagement around a clear product goal.
Manufacturing Software
Replace fragmented tracking with workflows for quotations, work status and supporting documents.
Real Estate Applications
Create listing, enquiry and internal coordination tools around your business process.
Business Operations
Build task, approval and reporting tools around the way your team works.
Process
A practical path from requirements to release.
Each stage ends with something you can review: a scope, a prototype, a working increment or a release.
01
Understand
Discuss your users, business needs, existing systems and delivery constraints.
02
Define
Agree scope, technical approach, responsibilities and a delivery plan.
03
Design and build
Develop in manageable increments with visible progress and review points.
04
Test and release
Validate agreed workflows, resolve release blockers and prepare deployment.
05
Support and improve
Document the handover, review production feedback and plan the next priorities.
Technology
Tools and platforms we work with
Pythonscikit-learnXGBoostLightGBMPyTorchTensorFlowpandasSparkAirflowMLflowdbtPostgreSQLBigQuerySnowflakeAWS SageMakerGoogle Vertex AIAzure ML
Cost
What shapes the cost of your project?
There is no useful price without scope. These are the factors we review when preparing an estimate, and the estimate lists every assumption so you can see what would change it.
01
Number of workflows
Each workflow needs its own prompts, tools, evaluation set and review design.
02
Data preparation
Cleaning, labelling, chunking and access rules often take more effort than the model itself.
03
Integrations
Connections to CRMs, ERPs, document stores and identity providers add design and testing work.
04
Evaluation and review
Building test sets, review queues and monitoring is essential for reliable results.
05
Model and hosting
Hosted APIs, open models and private deployment have very different setup and running costs.
06
Security and compliance
Sensitive data, audit needs and data residency add controls and documentation.
Engagement models
Choose the engagement that fits your next step.
Dedicated Team
For a changing roadmap or ongoing development. Agree roles and working process, then manage priorities through a shared backlog.
Best for team extension and long-term product work.
Scoped Project
For a defined product or milestone. Align on requirements, deliverables and acceptance criteria, with changes reviewed before they affect the plan.
Best for MVPs, integrations and focused releases.
Maintenance and Improvement
For software that already exists. Start with a technical review, then agree support routines and planned improvements.
Best for live applications and modernisation.
FAQ
Questions about machine learning development
How much data do we need?
It depends on the problem. Some models work with thousands of examples, others need more. We assess this in the data review before committing.
How long until we see results?
A baseline and first model can usually be evaluated early; production integration takes longer depending on systems involved.
Can you explain how the model makes decisions?
We use interpretable models or explanation techniques where transparency matters, such as risk decisions.
What happens when data changes?
Monitoring detects drift, and a retraining process keeps the model current.
Do we need a data warehouse first?
Not necessarily. We can start from existing databases and exports, then improve the pipeline as needed.
Can ML run inside our mobile app?
Yes, smaller models can run on-device using frameworks such as TensorFlow Lite or Core ML.
Can we begin with a focused scope?
Yes. We can review your current situation and agree a bounded first engagement before a wider build.
How are pricing and timelines determined?
We review requirements, technical complexity, team needs and delivery constraints before proposing an engagement. Estimates list assumptions and what may change the plan.
Can we discuss a project under an NDA?
Yes. Let us know before sharing confidential materials so we can agree confidentiality terms first.
Discuss your machine learning development needs
Share your goals, current situation and the help you need. We will review your message and suggest a practical next step.