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When Boeing builds an airplane, they build it so that it can fly in any conditions. You do not ground an airplane when you face a storm at a certain altitude, fix its avionics so that it can fly at a different altitude so as to weather the storm, and then put it in the air again. This is exactly what is happening in machine learning today and why batch model training, by itself, is a flawed approach. Not only does data drift make you pull your machine learning models from production in order to have them retrained, but your data scientists are always trying to find out when the models need to be taken down, when the storm is sufficiently bad in order to ground the plane. With adaptive machine learning your machine learning model is able to learn as data comes in and adapt accordingly. So, when a storm comes in, you easily change altitude and weather the storm. Add to this TAZI’s Human in the Loop technology (HITL) and you have Continuous Learning™, the ability to learn from the past, the present and the future.

HITL is an advanced feature that allows human knowledge to seamlessly interact with machine learning, making the latter better. So, if you know a storm is coming, you do not have to wait to change the route or ground the plane. Similarly, if you know a new regulation is going to be in place that will affect your models, you can feed this into your machine learning models. If you have 30 years of experience in your industry, you can feed your insights into your models as well, naturally.

TAZI’s patented Continuous Learning™ is a feature in all its products and solutions. Like in any other industry, machine learning needs robust, adaptable models that can learn from humans too, and not just machine learning models that need to be periodically retrained when life changes.

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