LFT Industries

Machine Learning

Classification, prediction, and recommendation models integrated directly into production applications and dashboards.

Machine learning work focuses on models that classify, predict, or recommend based on your data, deployed as part of a production application rather than a standalone analysis.

Who this is for

Businesses with enough historical or operational data to make prediction or classification genuinely useful — forecasting demand, scoring leads, personalizing content, or automatically categorizing incoming data.

Capabilities

  • Classification and tagging models
  • Predictive analytics on historical data
  • Recommendation systems
  • Model APIs that plug into existing dashboards and workflows

Delivery process

The prediction or classification target is defined precisely first, since that determines what data is needed and how success is measured. The model is then trained, evaluated, and deployed behind an API the application calls directly.

Relevant technologies

PyTorchKerasFastAPIAWS

Evidence

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FAQ

Common questions

What business problems does machine learning typically solve?
Classification (sorting or tagging data), prediction (forecasting an outcome from historical data), and recommendation (surfacing the most relevant option for a user) are the three most common patterns behind practical ML features.
How is a machine learning feature actually shipped to users?
The trained model is deployed behind an inference API and called from the application — usually to power a dashboard, a recommendation, or an automated decision — so the output reaches users through the product itself.

Ready to talk through your machine learning project?

Solutions are scoped around your vision, requirements, and budget. No fixed pricing, no obligation.