Whether businesses are using machine learning to perform predictive maintenance or create better retail shopping experiences, ML has the power to unlock value across a myriad of use cases. We're constantly inspired by all the ways our customers use Google Cloud AI for image and video understanding--everything from eBay's use of image search to improve their shopping experience, to AES leveraging AutoML Vision to accelerate a greener energy future and help make their employees safer. Today, we're introducing a number of enhancements to our Vision AI portfolio to help even more customers take advantage of AI. Performing machine learning on edge devices like connected sensors and cameras can help businesses do everything from detect anomalies faster to efficiently predict maintenance. But optimizing machine learning models to run on the edge can be challenging because these devices often grapple with latency and unreliable connectivity.

In a recent blog post, Google announced enhancements to a part of its Vision AI portfolio: AutoML Vision Edge, AutoML Video, and the Video Intelligence API. Each received updates to enhance their capabilities. Both AutoML Vision Edge and AutoML Video were both introduced earlier this year, in April, as a part of Google's AI Platform, while the Video Intelligence API introduction dates back a few years prior, with a public beta release in June 2017. We're constantly inspired by all the ways our customers use Google Cloud AI for image and video understanding--everything from eBay's use of image search to improve their shopping experience to AES leveraging AutoML Vision to accelerate a greener energy future and help make their employees safer. With AutoML Vision Edge, developers can train, build and deploy ML models at the edge.

With AutoML Vision Edge, you can create custom image classification models for your mobile app by uploading your own training data. Firebase ML Kit has a lot of features that allows you to perform machine learning on the user's phone. AutoML allows you to create a custom solution exactly for your problem, the best part is you don't need to know machine learning for building your solution. You just have to upload images and AutoML takes care of everything for you. In this blog post we will build an app called SeeFood, the app sees food and tells you what food item it is.

Whether businesses are using machine learning to perform predictive maintenance or create better retail shopping experiences, ML has the power to unlock value across a myriad of use cases. We're constantly inspired by all the ways our customers use Google Cloud AI for image and video understanding--everything from eBay's use of image search to improve their shopping experience, to AES leveraging AutoML Vision to accelerate a greener energy future and help make their employees safer. Today, we're introducing a number of enhancements to our Vision AI portfolio to help even more customers take advantage of AI. Performing machine learning on edge devices like connected sensors and cameras can help businesses do everything from detect anomalies faster to efficiently predict maintenance. But optimizing machine learning models to run on the edge can be challenging because these devices often grapple with latency and unreliable connectivity.

Roughly a year ago, Google launched Cloud AutoML, a platform for creating custom AI models, ahead of the debut of its ML Kit suite of machine learning tools for Firebase. Since then, the Mountain View company has slowly but steadily added prebuilt AI models addressing text translation, image classification, and other tasks to the portfolio. And today, it's updating a subset of those -- AutoML Vision Edge and AutoML Video -- with enhanced capabilities. Google also this week announced the rollout in beta of AI-based recommenders for Google Cloud Platform, which automatically suggest ways to make cloud deployments more secure and cost-effective without compromising performance. As of today, recommenders for Identity and Access Management (IAM) and Compute Engine are available to all Google Cloud customers.