The first-ever physical AutoML – The Intelligence Behind Seed-X Seed Sorting

In our age of AI-powered solutions, the latest deep learning algorithms beat the state-of-the-art results for almost any task and dataset. It is little wonder every industry and many companies are looking to implement AI and machine learning algorithms to solve their business needs.

But how can you leverage the power of AI, specifically machine learning, if your organization lacks the technical expertise? The answer is Automated Machine Learning or AutoML.

AutoML enables the development of state-of-the-art machine learning and deep learning algorithms with little to no technical knowledge, allowing anyone to harness the power of the latest algorithms to solve real-world problems. This automated process usually covers all aspects of the development pipeline, starting from processing the raw data, up to deploying the ML model into production.

AutoML services are offered by many cloud providers ,and offer a good solution for integrating the magic of ML without having to invest in developing the required in-house expertise. Nevertheless, while big tech companies and many cloud providers offer AutoML services, the offering begins and ends in the ‘virtual world’, with cloud platform, digital images (or other types of digital data e.g. text, tables etc.) and digital bits sent from place to place.

Seed-X has taken the AutoML idea one step further by offering its customers “physical” AutoML seed sorting. Why? Because in cases where actual seed sorting is needed, a fortiori with high throughput, the cloud based generic AutoML platforms are simply not fast enough, or accurate enough in terms of their results.

AutoML usually includes “Neural Architecture Search” (NAS), the process of automatically finding the best model for the task at hand, and “Hyperparameter Search”, the automatic process of finding the best values for the model hyperparameters.

By comparison, Seed-X’s proprietary procedure also includes many other parts:

  1. Multiple insights and analyses based on our proprietary algorithms and data. For instance, is more data needed in order to provide the required results?; what use cases/data/classifiers in our databases is related to the current task/data?; what other insights can we deliver for this specific data?; and many more.
  2. Options to set the goals and requirements of the actual seed sorting to optimize results.
  3. Latency/throughput automated optimizations, since the output classifier needs to sort physical seeds with real-time performance.

It would be reasonable to assume the resources and hardware requirements necessary to perform the above-described process are extensive indeed. In actual fact, Seed-X’s solution to this problem is remarkably resource lite, delivering seamless integration between the cloud and the physical Seed Sorter. We achieve this by delegating the heavy lifting to our cloud workers, and once this process is complete, the classifier is deployed automatically onto the Sorter, where the magic of ML-powered physical sorting can begin.

Customers will soon be able to see physical AutoML, the intelligence behind Seed-X’s Seed Sorting, in action.
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