ML Forecasting PoC
A custom ML forecasting model on your data.
What you get at the end of the engagement
Production-ready ML model
The model is trained on your data during the engagement. By the time the PoC ends, it is already running on your Databricks environment, not a prototype to be rebuilt later.
Using state-of-the-art solutions
The engagement uses the most recent ML frameworks and model architectures for time-series and deep learning forecasting, selected based on your data type, volume, and forecasting horizon.
Easy reproducibility
You receive the full model training code at handover. Your team can retrain the model on new data, adjust the features, or adapt the pipeline without starting from scratch or re-engaging.
Multiple data sources
Databricks data workflows allow the model to combine your internal historical data with external sources - market data, seasonality signals, third-party feeds - for more accurate predictions.
Where ML forecasting delivers results
Financial Planning
Predict future revenue, expenses, or investment returns based on historical time-series patterns and seasonal variations. ML forecasting captures non-linear relationships and external signals that statistical models miss.
Customer Lifetime Value
Analyse transaction time-series to estimate future revenue contributions over the customer's lifecycle. Enables personalised retention strategies and better budget allocation across segments.
Fraud Detection
Identify anomalies and irregular activities in transactional time-series patterns, enabling real-time intervention and reducing financial risk before exposure grows.
Your ML forecasting PoC, from planning to handover
- Kick-off & requirements alignment
- Access provisioning
- Scope confirmed, timelines set
- Business exploration
- Data exploration
- Feature candidates identified
- Train model
- Performance evaluation
- Iterative tuning
- Enablement sessions
- Deployment
- Business validation
- Documentation
One engagement.
Everything included.
Initial design planning
The model is designed around your data, not applied generically. Business requirements and available data sources (including external datasets) are assessed before training begins.
Easy deployment in focus
You receive both the trained model and its performance evaluation results. Deployment is part of the scope by the end of the engagement, the model is running in your environment.
Business validation
Making business users confident in the predictions is an explicit goal throughout the engagement, not a nice-to-have at the end.
