How do you deploy machine learning models to mobile, web, and edge devices?

Edge deployment is recommended for critical solutions where the results are expected to load in real-time or near real-time. To do so, it is crucial that the inferencing (prediction) of any model happens locally (not on cloud, rather on the edge device, sensor, hardware, gateway). For any edge deployment to work effectively, choosing the right model and it's type (framework) is important. The model cannot be resource intensive, rather it should be optimized & light-weight. With regards to the same, there are are multiple formats like TFLite, ONNX, Tensorflow, PMML, and more. The right format is chosen based on the edge device compatibility, integration flexibility, availability of right resources and more.

The framework chosen to deploy machine learning models can significantly influence the efficacy and efficiency of AI-driven solutions. In wound care, where timely and accurate diagnosis is paramount, it's crucial to opt for a framework that is robust, scalable, and offers real-time analysis capabilities. eKare, with its commitment to advancing wound care through technology, must prioritize frameworks that seamlessly integrate with medical databases, handle diverse wound images, and adapt to evolving care protocols. It's not just about selecting a popular or advanced framework; it's about aligning the tool with the unique needs of wound care professionals and patients to ensure optimal outcomes.

Optimizing your machine learning model is a pivotal step in ensuring that the AI-driven solutions deliver precise and consistent results. In the context of wound care and eKare's offerings, an optimized model translates to more accurate wound assessments, better prediction of healing trajectories, and timely alerts for complications. Regularly updating the model with fresh data, refining its algorithms, and continuous testing against real-world scenarios are crucial. This not only bolsters the reliability of AI-driven recommendations but also builds trust among healthcare professionals who rely on eKare's solutions for enhanced patient care.

Deployment at the edge for real-time classification or prediction applications may require individual inferences to run in 10 to 100 milliseconds on non-GPU targets. This constraint needs to be taken into account in choosing an ML model that is fast while not requiring to scale in terms of volume or variety of input data.

In the realm of wound care, where eKare's tools are utilized, converting a model can facilitate its integration into various medical devices or systems. For instance, a model designed initially for a standalone application might need conversion to be embedded into portable wound assessment tools. The conversion ensures that the algorithms operate seamlessly, delivering real-time insights irrespective of the device or platform in use. This adaptability amplifies the effectiveness and reach of eKare's AI-driven wound care solutions, enabling healthcare professionals to leverage technology across diverse settings.

You will need to do much more than testing your model by hand on your target device! You will need a test suite specifically tailored for ML so that you can monitor things like concept and data drift, which can include covariant drift, prediction drift, etc, as well as the typical unit and test driven software methodologies you might already be used to. Some popular tools for this are deepchecks, Drifter-ML, or even web based tools like weights and biases.

Testing a machine learning model is paramount to validate its efficacy, especially in critical areas like wound care. For platforms like eKare, it's essential to ensure that the AI-driven insights provided are both accurate and consistent. In wound care, an inaccurate model could lead to misdiagnosis or inappropriate treatment recommendations, which can have dire consequences for patient health. Testing involves subjecting the model to various real-world scenarios and data sets to gauge its performance. By refining the model based on these tests, eKare can deliver a tool that healthcare professionals can trust, ensuring that patients receive the best possible care informed by reliable data-driven insights.

Obfuscation is not security. You need to treat your customers like an adversary that is 100x smarter than you are, because some of them are.

Securing a machine learning model is of utmost importance, especially in sensitive sectors like healthcare. For solutions like eKare in wound care, ensuring that patient data and AI-driven analyses are protected from breaches or malicious attacks is crucial. The confidential nature of health records necessitates stringent security protocols. Furthermore, a compromised model could lead to incorrect diagnoses or recommendations, jeopardizing patient care. eKare, by prioritizing the security of its AI model, not only safeguards patient data but also reinforces the trust healthcare professionals place in its technology, ensuring that AI-driven insights remain both reliable and secure.

This is often neglected by project teams. The AI model need to be frequently monitored to check that the performance is not dropping. In the case of edge AI, we also need that the different sensors are still properly working and that the connection between the different local data sources is still established.

Deploying machine learning models to mobile, web, and edge devices involves converting trained models into formats suitable for these platforms, like TensorFlow Lite for mobile and ONNX for edge devices. Next, you integrate the models into your app or platform using appropriate programming languages and frameworks. Optimizing for resource constraints, considering factors like model size, latency, and power consumption, is crucial to ensure smooth and efficient inference across diverse devices and environments.