“They chose the right general approach to machine learning, but it wasn’t performing as well as they’d hoped,” says Sandeep Chitta, a Slalom analyst focused on advanced analytics.
Veripad partnered with Slalom on a proof-of-concept to improve the model with Amazon SageMaker. This powerful new platform fast-tracks the process of setting up a machine learning environment and training a solution.
“We developed Amazon SageMaker to remove all of the complexity and inefficiency that’s involved in machine learning to make it easy for developers to easily get started and become skilled building, training, and deploying models, “ says Joel Minnick, AWS’s machine learning and AI marketing leader.
For Veripad’s challenge, we agreed on a modular approach of dividing each PAD image into its 12 lanes and processing them separately. This approach would make the solution more robust and support other PADs in the future. We worked with Veripad to apply image processing techniques like noise cancellation and contrast change to improve the quality of each image. From there, we used clustering techniques to differentiate color patterns and finally logistic regression to classify the type of drug and also assess its purity.
Five weeks later, the new model could “read” a PAD in a matter of seconds and report back with 90% confidence, 5-10% higher than human experts.
“We’re on a good path,” says Jason Ki, Veripad CTO and co-founder. “Our short time with Slalom laid a foundation for us to continue working. We want to get close to 100% accuracy.”