As known to many, Google has recently released Model Search which is an open-source platform. This caters to developing efficient and best machine learning models automatically. Rather than focusing on a particular domain, Model Search is domain agnostic and flexible beyond imagination. Well, not just that. To our surprise, it is even capable of finding just the right architecture. With this, it will best fit a given dataset and the associated problem. At the same time, it is mastered enough to minimize the time that goes behind in coding, the effort as well as the resources that are put in.
Model Search is built on Tensorflow. It is flexible to the extent that it can run either on a single machine or in a distributed setting. This feature does set it apart from the rest, without any doubt. It is equipped with multiple trainers, a search algorithm, a transfer learning algorithm and a database as well that aims at storing the various evaluated models.
Talking about the architecture of Model Search, it is based on four foundational components: They are –
All in all, The Model Search Code aims to provide the researchers with a flexible, domain-agnostic framework to develop the most efficient and best machine learning model. Also, the framework is so powerful that it can build models with state-of-the-art performance. It also has the capability to deal with well-known problems when provided with a search space composed of standard building blocks.