End to End MLOp system for pre-clinical medical research
Marco GARZOLA, Digital Innovation Manager , Tecniplast
Tecniplast core business is mainly generated by special plastic cage production for laboratory animals and customer support for big pharma and Academia Institutions in the pre-clinical industry sector.
Nowadays, operators accessing clean rooms where laboratory animals are housed create several drawbacks, such as the need for sterility of any operation, induced stress of the animals due to the visual inspection requested for welfare purposes by regulatory bodies, difficulty in detecting animal problems because they are nocturnal animals with consequent latency of human interventions and associated errors, which, in the end, affect animal welfare management, Facility productivity, costs, and scalability of services.
Current Tecniplast digital solutions are based on capacitive sensors technology that generates data sent to the cloud, where they are processed. This paradigm is sustainable since their data transfer is limited in size. Unfortunately, it rapidly becomes unstainable if more complex sensors (video, audio) are introduced since generating a huge amount of data.
Recognizing these challenges as opportunities, in 2019, the Tecniplast Innovation team started the tiny, embedded camera project for laboratory animal welfare management, exploring it with local monitoring end-to-end system application. End-to-end means 1) production-ready hardware architectures and boards, including micro-controllers and sensors; 2) embedded firmware; 3) connectivity protocols, 4) cloud dash-board and data storage/processing.
In this way, Tecniplast customers will get the following benefits: massively scale monitoring operations, steadily improve sterility of medical protocol applications, reduce costs, and introduce a new way of machine learning services.
In partnership with STMicroelectronics expertise on TinyML we started the ML development for the hardware designed and deployed tinyAI intelligence onto the embedded boards themself. This approach simplified the end-to-end SW architecture and, even more relevant, reduced the cloud-associated costs, which were unsustainable to the scale.
Currently, the pre-production system is up and running with field tests of 500 boards from which we can collect data to fine-tune our tiny NN solutions; every MCU runs 3 NNs with 3 different purposes. In the next future, new solutions with tinyAI will be introduced for a wider range of sensors.
This project so far has been a remarkable MLOps example encompassing from cloud to the embedded deployment with all the required steps from problem definition to field trials, passing through data acquisition, labeling, tinyML design, and mapping on low-power MCU.
The optimized product today encompasses not only the embedded device but also the SW/Cloud and the mechanical parts.
That’s the way we conceived a close-to-production system in Tecniplast, and I am very happy to share my experience within the TinyML community, including the lessons learned.
