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Model Search by Google automatically optimizes and identifies AI models

Google announced the release of Model Search, an open-source platform designed to help researchers develop ML models efficiently and automatically. Instead of focusing on a specific domain, Google’s Model Search is domain-agnostic, making it capable of finding a model architecture that fits a dataset and problem while minimizing coding time and compute resources.

The success of an AI model often depends on how well it can perform across various workloads. But designing a model that can be simplified is extremely challenging. Recently, AutoML algorithms have emerged to help researchers find the right model without the need for manual experimentation. However, more often than not, these algorithms are compute-heavy and need thousands of models to train.

Model Search — which is built on Google’s TensorFlow ML framework. It can either run on a single machine or several, it consists of multiple trainers — a transfer learning algorithm, a search algorithm, and a database to store evaluated models. Model Search runs training and evaluation experiments for Artificial Intelligence models in an adaptive and asynchronous fashion, such that all trainers share the knowledge gained from their experiments while carrying on each experiment independently. At the beginning of every cycle, the search algorithm looks up all the completed trials and decides what to try next, after which it “mutates” over one of the best architectures found up to that point and assigns the resulting model back to a trainer.

To further improve efficiency and accuracy, Model Search employs transfer learning during experiments. For instance, it uses knowledge distillation and weight sharing, which bootstraps some of the variables in models from formerly trained models. This allows faster training and by extension opportunities to learn more and apparently better architectures.

After a Model Search run, users can compare the many models found during the search. Additionally, they can make their own search space to modify the architectural elements in their models.

Google says that in an internal experiment, Model Search improved upon production models with negligible iterations, mainly in the areas of keyword spotting and language identification. It also managed to find an architecture appropriate for image classification on the heavily explored CIFAR-10 open-source imaging dataset.

Google research engineer Hanna Mazzawi and research scientist Xavi Gonzalvo wrote in a blog post. “We hope the Model Search code will provide researchers with a flexible, domain-agnostic framework for machine learning model discovery. By building upon previous knowledge for a given domain, we believe that this framework is powerful enough to build models with state-of-the-art performance on well-studied problems when provided with a search space composed of standard building blocks.”

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