Machine Learning Development — Custom Models, Built to Deploy
Machine learning development is the end-to-end process of building custom predictive models — data preparation, feature engineering, training, validation and deployment — tailored to a specific business problem. Perimattic delivers this full lifecycle as a fixed-scope engagement.
Overview
What is ModelWorks?
Machine learning development builds custom models that learn patterns from your data to predict outcomes, classify inputs, detect anomalies or make recommendations. Unlike off-the-shelf AI, these models are trained on your data for your problem. Perimattic's ModelWorks covers the full lifecycle from data prep to production deployment.
Not every problem is a language problem. Forecasting demand, scoring risk, detecting fraud or defects, recommending the next action — these are classic machine learning problems, and they're often where the hardest, highest-value engineering lives. ModelWorks is the deep-engineering tier of the suite: we take your data and your problem and build a model that measurably beats the status quo, then deploy it and set up the monitoring to keep it honest as data drifts. This is the service that proves we do the hard stuff — which is what makes the rest of the suite credible.
ModelWorks is honest about what data can and can't support. If your data is too small, too dirty, or too weakly correlated with the target, we say so at scoping — not three months in when the model refuses to work. That honesty is why the fixed window holds: we only commit to the engagement when the data justifies it.
What You Get
What you get
A custom ML model built for your problem and deployed into production: data preparation, feature engineering, model training and validation, deployment, and monitoring. You get a model that performs against agreed metrics — not a research notebook. Every model ships with a monitoring path so drift and performance degradation are surfaced before they cause bad decisions.
Scope
Fixed scope — what is and is not included
| Not included | Included |
|---|---|
Large-scale data collection or labelling (available as an add-on) | Problem framing against a target metric |
Ongoing retraining beyond the handover window (available as a retainer) | Data preparation and feature engineering |
Guaranteed accuracy beyond what the data supports | Model selection, training and rigorous validation |
Feature store implementation from scratch (we can plug into an existing one) | Performance evaluation against agreed metrics |
Deployment to production with an inference path | |
Monitoring setup for performance and drift detection | |
Handover documentation and knowledge transfer |
| Timeline | Best for | Tech stack |
|---|---|---|
| 6–10 weeks | Forecasting, risk and fraud scoring, churn prediction, defect and anomaly detection, recommendation systems, quality prediction | scikit-learnXGBoostPyTorchMLflowCloud ML infrastructure (SageMaker, Azure ML, Vertex AI) |
Process
How it works
Problem and data framing
We define the target metric and assess whether the available data actually supports it. This is where we say yes, no, or "not until you collect more data." Skipping this step is why most ML projects fail.
Data prep and features
Clean, transform, engineer features. This is usually the longest part of any ML engagement — the model itself is often the easy bit. Feature engineering is documented so your data team can extend it.
Train and validate
Build candidate models, validate rigorously with cross-validation and hold-out sets, compare against a simple baseline. Every candidate is evaluated on the agreed metric — no accuracy theatre.
Deploy
Ship to production with an inference path — batch, real-time, or a hybrid depending on the use case. Deployment includes model versioning through MLflow so rollbacks are one step, not a rebuild.
Monitor
Track performance and drift on live data. Alerts fire when the model degrades or the input distribution shifts. Optional retainer for ongoing retraining as drift accumulates.
Use Cases
Use cases
These are the ML problem shapes we build most often. Each has a clear target metric, a well-defined success criterion, and a measurable outcome — which is why they are buildable as fixed-scope engagements.
Demand and sales forecasting
Predict future demand or sales at SKU, region, or channel granularity. Common across manufacturing, retail, distribution, and consumer goods. The forecast feeds into production planning, inventory, and staffing.
Churn prediction
Score which customers are at risk of leaving in the next N days, so retention teams focus effort where it matters. Every SaaS, subscription, and services business has a churn model in it — the question is whether it is deliberate.
Fraud and anomaly detection
Flag unusual transactions, sessions, or events for human review. Financial services use it for card fraud; manufacturing uses it for defect detection on production lines; IT ops uses it to catch outages before customers do.
Predictive risk scoring
Assign a numerical risk score to a new applicant, a new project, or a new supplier. Used in lending, insurance underwriting, procurement, and project portfolio management.
Recommendation engines
Suggest the next best product, next best article, next best action. Every e-commerce and content business has one — the well-tuned ones are visibly better than the generic Amazon-style default.
Quality prediction
Predict which units in a manufacturing run will pass or fail final inspection, using upstream process data. Reduces waste by catching bad units before they consume more processing.
Results
Proven in production
Machine learning models shipped by Perimattic are running in production across forecasting, anomaly detection, and recommendation use cases. Every deployment includes drift monitoring and a documented retraining path, which is what makes production ML different from a one-off notebook. The engagements that work best start with clean framing — a specific metric to move, a data set that can support it, and an operational owner who will actually use the outputs.
Why Us
Why Perimattic for machine learning development
Full-lifecycle, deployment-first ML from a team that has been building production systems since 2018 — DevOps and monitoring first, then full-stack, then ML. Not a model that dies in a notebook. Straight answers on what the data can and cannot support, which is unusual in a services market where a "yes" is often the default. Enterprise-grade defaults — secure inference paths, versioned models, drift monitoring — come standard.
Frequently Asked Questions
Frequently asked questions
What is the difference between ML and generative AI?
Classical machine learning learns patterns from structured data to predict outcomes — a demand forecast, a churn probability, a fraud score. Generative AI (large language models, diffusion models) generates content — text, code, images. Both are "AI," but they solve different problem shapes. ModelWorks focuses on classical ML; AgentForge and KnowledgeRAG focus on LLM-powered systems. Many production systems use both.
How much data do we need?
It depends on the problem. Rule of thumb: forecasting typically needs at least 18–24 months of history to capture seasonality; classification typically needs at least several thousand labelled examples per class; recommendation systems need consistent interaction data over time. The honest answer is "we'll assess your data at scoping and tell you if it is enough." If it is not, we say so and recommend a data collection plan first.
How do you measure success?
Against an agreed metric documented at scoping. Every ModelWorks engagement names the metric — RMSE for forecasting, F1 for classification, precision-at-k for recommendation — and the baseline the model must beat to be considered successful. Success criteria are set with the operational team who will use the outputs, not just the data team.
Do you deploy the model or just build it?
We deploy. A model that lives in a notebook is a demo, not a system. ModelWorks includes production deployment on your chosen infrastructure — batch, real-time, or hybrid — with monitoring, versioning through MLflow, and a documented inference path. Handover includes a runbook so your team can operate it without us.
How do you handle model drift?
Drift monitoring is included by default. Alerts fire when the input distribution shifts, when the model performance degrades against the target metric, or when the model starts producing unusual output distributions. Retraining is not automatic — humans decide when to retrain against what data — but the monitoring surface makes those decisions timely instead of reactive.
How long does an ML project take?
Fixed 6–10 weeks. Simpler problems with clean, ready-to-use data land at the shorter end; harder problems requiring extensive feature engineering, ensemble modelling, or novel architectures land at the longer end. The window is committed at engagement start.
What does it cost?
ModelWorks is quoted as a fixed scope against problem complexity, data readiness, and deployment target. Retraining retainers are available for ongoing drift management. Contact us for a costed proposal.
Related
Related in the Perimattic AI Suite
ModelWorks pairs naturally with these sibling modules.
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