AI & ML Cloud Computing & SaaS News

Google aims to bring AI to every business with Cloud AutoML release

Google announced Cloud AutoML, a product that enables businesses with limited machine learning expertise to build high quality, custom AI models to improve their product or service. Cloud AutoML is a simple, secure and flexible ML service that lets businesses and developers train custom vision models for their own use cases.

To make AI accessible to every business, businesses with limited ML expertise can leverage Cloud AutoML to start building their own high quality custom models by leveraging advanced techniques like learning2learn and transfer learning from Google. Cloud AutoML will make AI experts even more productive, advance new fields in AI and help less-skilled engineers build powerful AI systems.

Google’s first Cloud AutoML release is Cloud AutoML Vision, a service that makes it faster and easier to create custom ML models for image recognition. Its drag and drop interface lets users easily upload images, train and manage models, and then deploy those trained models directly on Google Cloud.

Specific advantages of using 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, one 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 businesses specify data, then turns that data into a high quality model customized for specific needs.

According to Jia Li, Head of R&D, Cloud AI and ML, “At Google Cloud, our goal has been to lower the barrier of entry and make AI available to the largest possible community of developers, researchers, and businesses. Our Google Cloud AI team has been making good progress towards this goal.”

“AutoML Vision is the result of our close collaboration with the Google Brain and other Google AI teams, and is the first of several Cloud AutoML products in development that aims to make it easier for more businesses to adopt ML. While we’re still at the beginning of our journey to make AI more accessible, we’ve been deeply inspired by what our 10,000+ customers using Cloud AI products have been able to achieve. We hope the release of Cloud AutoML will help even more businesses discover what’s possible through AI,” she further added.

Cloud AutoML’s technology is already enabling businesses such as Disney Consumer Products and Interactive Media to build vision models to annotate its products with Disney characters, product categories, and colors. These annotations are being integrated into their search engine to enhance the impact on Guest experience through more relevant search results, expedited discovery, and product recommendations on shopDisney.

The Zoological Society of London (ZSL) is an international conservation charity devoted to the worldwide conservation of animals and their habitats. A key requirement to deliver on this mission is to track wildlife populations to learn more about their distribution and better understand the impact humans are having on these species. To achieve this, ZSL has deployed a series of camera traps in the wild that take pictures of passing animals when triggered by heat or motion. The millions of images captured by these devices are then manually analyzed and annotated and with the relevant species such as elephants, lions, and giraffes, etc., which is a labor-intensive and expensive process. ZSL’s dedicated Conservation Technology Unit has been collaborating closely with Google’s CloudML team to help shape the development of this exciting technology, which ZSL aims to use to automate the tagging of these images—cutting costs, enabling wider-scale deployments, and gaining a deeper understanding of how to conserve the world’s wildlife effectively.

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