AI and Deep Learning Projects: From Experiment to Application

AI and Deep Learning Projects: From Experiment to Application

This is a description of a project type we deliver: taking a machine learning capability that works in a notebook and turning it into something a business can rely on.

The Challenge

A promising experiment existed, or a manual process was clearly suited to automation. What did not exist was the path between them: data collection at scale, an evaluation that reflected real cases, an interface for the people using the output, and any form of monitoring once the model met reality.

Our Approach

Discovery fixed the question and its measure before any modelling: what decision the system supports, what an error costs, and what accuracy makes it useful. Data was audited for volume, quality and permitted use, and the human review boundary was drawn based on consequence.

Solution

A data pipeline feeding model training, an evaluation built on held-out data and the cases the business cares about, and a serving layer exposing the capability through an API. Reviewers work through a queue with the evidence beside each decision, and their corrections are recorded as data for the next version.

Technology

Selected for what the team can maintain: a mainstream modelling ecosystem, a service layer for serving predictions, storage that keeps inputs and outputs auditable, and monitoring for drift in both inputs and accuracy.

Implementation

Deployed in shadow mode first, where the system ran alongside the existing process without being trusted with decisions, then widened as measured performance held.

Results

Accuracy, review volume and time saved are reported against the baseline agreed at discovery, and published only where verified and approved.

Have a model that needs to become a product? Tell us about your project and we will start with the measure.

Project details in this article are deliberately general. Client names, verified results and references are published only with the client's approval.