Forecasting & machine learning

Models that stay in production and keep being retrained, not a notebook that was accurate once.

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What is it?

Demand forecasting, churn and risk scoring, predictive maintenance. The modelling is usually the short part; the work is the data pipeline feeding it, the deployment path, and the monitoring that catches drift before the business notices the numbers have stopped making sense.

For a technical reader

For most enterprise forecasting, gradient-boosted trees over well-constructed features beat deep learning, and classical methods such as ETS or ARIMA remain a serious baseline worth measuring against. The engineering around the model matters more than the algorithm: a feature pipeline that computes the same transformations at training and serving time, versioned datasets so a result can be reproduced, and monitoring on input distributions as well as output accuracy.

Our services

Feasibility assessmentWhether the data you hold can support the prediction you want, answered before a project is committed.
Model developmentBaseline, candidate models, and honest evaluation on held-out data with the business metric, not just RMSE.
Production deploymentServing infrastructure, feature pipelines and the integration into the workflow that acts on the prediction.
MLOps foundationVersioning, reproducible training, drift monitoring and retraining automation.

What’s covered

Demand forecasting
SKU and location level forecasts, with seasonality and promotion effects separated out
Churn and propensity
Scoring that feeds a retention or sales workflow rather than sitting in a report
Predictive maintenance
Sensor and telemetry models that flag equipment before it fails
Anomaly detection
Fraud, error and outlier detection on transaction and operational streams
Feature pipelines and MLOps
Versioned features, reproducible training and automated retraining triggers

Business problems we solve

Forecasts are a spreadsheet and a gut feelReproducible, measurable models replace that, with the current approach kept as the baseline to beat.
A model was built but never deployedUsually missing the serving path and the workflow integration. We treat deployment as part of the work, not a handover.
Accuracy decayed and nobody noticedDrift monitoring and retraining triggers are set up at build time.
We cannot explain a predictionFor regulated decisions, feature attribution and documented limits are part of the deliverable.

Our delivery approach

  1. 01
    Set the baselineWe start with the simplest reasonable approach and measure it. A model that cannot beat last-week's-number is not worth deploying.
  2. 02
    Build the feature pipelineReproducible, versioned, and identical between training and serving. Training-serving skew is the most common silent failure.
  3. 03
    Deploy behind a decisionA prediction nobody acts on has no value. We wire the output into the actual workflow.
  4. 04
    Monitor for driftInput distributions and accuracy are tracked, with retraining triggers defined up front.

Solutions & project types

Examples of the work we take on in this area. These describe project shapes, not named client engagements.

SKU-level demand forecastForecasts by product and location feeding replenishment, with promotion and seasonality separated.
Churn scoringScoring that triggers a retention workflow rather than producing a monthly report.
Predictive maintenanceEquipment telemetry models flagging likely failure ahead of a maintenance window.
Transaction anomaly detectionFlagging outliers for review in finance or operations streams.

Technologies & platforms

Modelling

  • scikit-learn
  • XGBoost
  • LightGBM
  • Prophet
  • statsmodels
  • PyTorch

Pipelines

  • Airflow
  • Dagster
  • dbt
  • Spark

Serving & tracking

  • MLflow
  • SageMaker
  • Vertex AI
  • Azure ML
  • Oracle Data Science

Data

  • Snowflake
  • BigQuery
  • Databricks
  • PostgreSQL

Monitoring

  • Evidently
  • Prometheus
  • Grafana
Learn this field

Where to start with forecasting & machine learning.

Secondary to the work itself, but genuinely useful if you are building the skill. Every link goes to official documentation or an official channel.

Beginner

  • Python with pandas and numpy
  • Descriptive statistics and distributions
  • Train/validation/test splits and why leakage ruins results
  • Fit a regression and a tree model, compare them
  • Plot your data before modelling it

Intermediate

  • Feature engineering and encoding categorical data
  • Cross-validation, including time-series splits
  • Class imbalance and the right metric for the problem
  • Hyperparameter search
  • Packaging a model behind an API

Advanced

  • Forecasting hierarchies and reconciliation
  • Causal inference versus correlation in business decisions
  • Feature stores and training-serving consistency
  • Drift detection and automated retraining
  • Explainability with SHAP and its limits

Certifications

  • Google Cloud Professional Machine Learning Engineer
  • AWS Certified Machine Learning Engineer – Associate
  • Microsoft Certified: Azure Data Scientist Associate
  • Databricks Certified Machine Learning Associate

Practice projects

  • Forecast a public time series and beat a naive last-value baseline
  • Build a feature pipeline that runs identically in training and serving
  • Deploy a model behind an API and log every prediction
  • Simulate drift by shifting the input distribution and see if monitoring catches it
  • Explain five individual predictions with SHAP and sanity-check them
FAQs

Questions we get asked.

How much data do we need?

It depends on the signal strength and how many categories you are predicting across. For demand forecasting, two to three years of history is a reasonable starting point. The feasibility assessment exists to answer this for your case specifically.

Do we need a data scientist on staff?

To build, not necessarily. To keep it working, someone has to own it. We can run it as a managed service or train an owner, but an unowned model degrades.

Will it be better than our planners?

Sometimes, often not on its own. The realistic gain is removing routine forecasting work so planners spend their judgement on exceptions. We measure against the current process rather than against zero.

What about generative AI for this?

Different tool. Language models are weak at numeric forecasting and expensive for it. They are useful for explaining a forecast or querying it in words, which is a separate piece of work.

AI & Data

Related services.

GenAI assistants & copilotsAssistants grounded in your own content, with the evaluation to show whether they are actually right.Data platform & warehousingOne place where the numbers agree, with lineage back to the source.Document & contract intelligenceExtracting structured, checkable data from contracts, invoices and forms at volume.Data governance & guardrailsKnowing what data you hold, who can reach it, and what the AI systems are allowed to do with it.
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