A new service by Google named Cloud AutoML uses several machine-learning tricks to automatically build and train a deep-learning algorithm that can recognize things in images. The initial release of AutoML Cloud is limited to image recognition. Its simple interface lets you upload images with ease, train and manage them, and finally deploy models on Google Cloud.

The technology is limited for now, but it could be the start of something big. Building and optimizing a deep neural network algorithm normally requires a detailed understanding of the underlying math and code, as well as extensive practice tweaking the parameters of algorithms to get things just right. The difficulty of developing AI systems has created a race to recruit talent, and it means that only big companies with deep pockets can usually afford to build their own bespoke AI algorithms.

In addition, rather than forcing enterprises to train their algorithms using Google’s data, Cloud AutoML ingests enterprise data assets and tunes the model accordingly. The key here is that Google helps enterprises to customize a model without having to do so de novo: There’s already a great deal of training baked in. Though initially focused on image data, Google plans to roll out the service to tackle text, video, and more.

Cloud AutoML Vision is built on Google’s transfer learning and neural architecture search technologies (among others). Disney has already started using the technology to annotate their products to improve the customer’s experience on their shop-Disney site. The Zoological Society of London is also using AutoML Vision to recognize and track wildlife in order to understand their distribution and how humans are impacting the species.

The video below simplifies and formulates the working of Cloud AutoML Vision.