Google unveils Cloud AutoML to automatically create AI models

By : |January 18, 2018 0
Google

Google, on Wednesday unveiled Cloud AutoML– a tool designed to simplify the process of deploying AI in business applications. The service helps developers — including those with no machine learning (ML) expertise — build custom image recognition models.

Cloud AutoML, that’s already being used by companies including Disney and Urban Outfitters to make search and shopping on their websites more relevant, is starting with image recognition, allowing customers to drag in images and train their systems to recognize them on Google’s cloud.

“Currently, only a handful of businesses in the world have access to the talent and budgets needed to fully appreciate the advancements of ML and AI,” Jia Li, head of research and development for Google’s cloud AI unit, and Fei-Fei Li, the group’s chief scientist, wrote in a blog post. “We believe Cloud AutoML will make AI experts even more productive, advance new fields in AI and help less-skilled engineers build powerful AI systems they previously only dreamed of.”

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Machine learning systems that create custom models, especially in the vision space, are nothing new. What sets Google’s apart is its ability to create a bespoke model without human intervention, something that the company helped pioneer. Salesforce is working in a similar space with its own AI products, but it’s difficult to determine how similar the two are at this juncture.

Google’s system is based on supervised learning, which means companies need to provide a bunch of data with labels to tell Cloud AutoML what it’s looking at and help it create a model. Google has a crowdsourced system available that will use humans to generate labels for unlabeled data, but companies can also provide their own labeled data when they first set the system up.

To get access to AutoML Visions, developers currently have to apply for access. The company didn’t share any pricing information yet, but chances are it will charge one fee for training the models and then another for accessing the model through its APIs.

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