Google’s Cloud AutoML helps businesses with limited ML expertise start building their own high-quality custom models.
Google has announced the released of Cloud AutoML, a service designed to automate machine learning and make it more accessible to businesses. In a blog post, the company said they created the service because very few businesses have the talent or budget necessary to fully embrace ML and artificial intelligence, and even those that do still have to grapple with building their own ML model.
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To simplify the process, they created Cloud AutoML, currently in alpha mode. Cloud AutoML uses existing Google services like learning2learn and transfer learning to help businesses lacking in ML experience start building their own models.
The first part of Cloud AutoML will be Cloud AutoML Vision, which is designed to simplify the process of creating custom models for image recognition. It uses a a drag-and-drop interface that allows the user to easily upload images and train and manage models, then deploy them directly on Google Cloud.
According to Google, key features of Cloud AutoML Vision include:
- Increased accuracy: Cloud AutoML Vision is built on Google’s leading image recognition approaches, including transfer learning and neural architecture search technologies. This means you’ll get a more accurate model even if your business has limited machine learning expertise.
- Faster turnaround time to production-ready models: With Cloud AutoML, you can create a simple model in minutes to pilot your AI-enabled application, or build out a full, production-ready model in as little as a day.
- Easy to use: AutoML Vision provides a simple graphical user interface that lets you specify data, then turns that data into a high-quality model customized for your specific needs.
“Urban Outfitters is constantly looking for new ways to enhance our customers’ shopping experience,” says Alan Rosenwinkel, Data Scientist at URBN. “Creating and maintaining a comprehensive set of product attributes is critical to providing our customers relevant product recommendations, accurate search results, and helpful product filters; however, manually creating product attributes is arduous and time-consuming. To address this, our team has been evaluating Cloud AutoML to automate the product attribution process by recognizing nuanced product characteristics like patterns and neckline styles. Cloud AutoML has great promise to help our customers with better discovery, recommendation and search experiences.”
