---
title: "Machine Learning & Predictive Analytics"
canonical: https://wavect.io/services/machine-learning/
language: en
description: "Custom machine learning for forecasting, classification and anomaly detection. Wavect validates data, compares baselines and integrates measurable predictions."
image: "https://wavect.io/img/general/bak/open_graph_preview.jpg"
---

Machine Learning

# Build a forecast or prediction you can test against a real decision.

A useful prediction changes what someone does next. We help define that decision, test whether the available data can support it and build the model and integration needed to use the result. A simpler baseline remains a serious candidate.

THE ENGAGEMENT

## A decision, a baseline and a testable model.

1. **Define success** Agree the action, target variable and cost of each error.
2. **Test the evidence** Check history, leakage, missing values and a realistic holdout.
3. **Use the prediction** Integrate the approved model with monitoring and fallback behavior.

DIRECT ANSWER

This service develops and evaluates models for forecasting, classification and anomaly detection, then connects approved predictions to a business workflow. Scope starts with data suitability, a measurable baseline and the cost of different errors.

Best for

- Teams with historical data and a repeatable decision to improve
- Products needing a forecast, score or anomaly signal with measured limitations

Consider another approach

- A chatbot or retrieval interface over documents
- A dashboard that summarizes what has already happened

## Work backwards from the operational choice

Demand forecasting should inform a replenishment decision; an anomaly score should route a review. We agree who acts, when the prediction is available and what happens when it is wrong. The target must be measurable from data available at prediction time. Reporting and source pipelines remain a [Data Engineering](/services/data-engineering/) engagement where needed.

## Check whether the history represents tomorrow

We inspect coverage, changes in business rules, missing outcomes and leakage from future information. A time-based split may be needed for forecasts; related customers or devices may need to stay together across splits. The assessment records which populations and conditions the data can support and where collecting more evidence is the better next step.

## Compare against the decision you use today

We establish a simple baseline before adding model complexity. Evaluation includes business-relevant errors, important subgroups and uncertainty, with thresholds chosen for the intended action. A model that improves an average metric but fails on a costly segment may be rejected. The result is a comparison you can inspect, not an isolated accuracy percentage.

## Illustrative acceptance: forecast demand without future information

An example test trains only on history available before a cutoff and forecasts a later period. Results are compared with a seasonal baseline and broken down by product group. A newly launched item is flagged as insufficiently supported. Acceptance records the errors, operational implications and whether the model is suitable for the proposed ordering workflow.

## Make deployment reversible

We deliver the data transformations, model artifact, evaluation record and an interface for predictions. Batch scoring may be sufficient; real-time serving is justified only by the workflow. We define fallback behavior, monitoring and a retraining decision owner. Ongoing model versioning, deployment and drift workflows can continue under [MLOps](/services/mlops/).

## Questions before you start

### How much data do we need?

There is no useful universal row count. Suitability depends on the target, variation, outcome quality and how representative the history is. We assess those factors before proposing training.

### Will you always build a custom model?

No. Rules, existing models and simple statistical methods are compared where appropriate. The chosen approach must earn its additional operating cost.

### Can predictions make decisions automatically?

Only within an agreed decision policy. We define thresholds, review paths and permissions separately from the model score, especially when an incorrect action carries material cost.

### What if the model does not beat the baseline?

The evaluation still provides a decision: improve data, change the target, retain the current method or stop. Further implementation is agreed after reviewing that evidence.

## Continue with the right scope

- [Prepare reliable analytical data](/services/data-engineering/)
- [Operate trained models over time](/services/mlops/)
- [Evaluate an image-based use case](/services/computer-vision/)

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