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to test your concept
of the PoC cost on the MVP build
focused on successful delivery
Develop a proof of concept to evaluate whether an AI system can be used to improve verification qualify of manual human covid test verification.
Trained object detection and image classification models using Google's cloud machine learning services on existing LFT datasets.
Near 99% accuracy achieved with the classification model, but more negative <redacted> samples were required for validation.
Inconclusive, incomplete, or faulty tests were not addressed in the models.
AI driven labelling can reduce costs by 40-50%
Placing the model inside the verification process can reduce human error.
Using cloud based machine learning services could speed up AI development.
Production ready development would take a further 4 months.
Stakeholders able to plan against "getting left behind".
Aim to roll out alongside human verifiers to build confidence in the model before increasing dependency on AI.