Roughly a year ago and ahead of the launch of its ML Kit collection of machine learning tools for Firebase, Google debuted Cloud AutoML, a cloud platform for creating custom AI models. Since then, the Mountain View company has nearly continually added prebuilt AI models addressing text translation, image classification, and other use cases to its suite’s 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.
AutoML Vision Edge
On the AutoML Vision Edge side of the equation, Google says that AutoML Vision Edge can now perform object detection as well as image classification on edge devices, including those using Nvidia, ARM, or other chipsets and running operating systems like Android and iOS. One customer — Tryon, an AI-enabled startup specialized in designing and producing augmented reality software for jewelry ecommerce and retail stores — has already tapped it to power an augmented reality shopping experience.
Above: Tracking traffic patterns with the new object recognition capabilities of AutoML Video.
Image Credit: Google
In a related development, Google says that AutoML Video, which a toolset designed to make it easier for businesses to train custom models to parse video content for specific things, can now track the movement of multiple objects between frames. Moreover, the complementary Video Intelligence API can detect and recognize the logos of over 100,000 popular businesses and organizations, which Google characterized in a blog post as a boon for brand safety, ad placement, and sports sponsorship use cases.
AI-based recommenders
One of the first of the aforementioned Google Cloud recommenders out of the gate is the IAM Recommender, which detects overly permissive access policies in a given organization and makes adjustments based on the patterns of similar users. Google says the suggestions are generated by analyzing the IAM permissions for each customer individually to create an overall model to recommend more secure IAM policies, taking into account the environment.
As for one of the other recommenders available in beta — the Compute Engine Rightsizing Recommender for VM instances — it analyzes processor and memory utilization over the previous eight days to identify the right machine type for the workload, preventing the provisioning machines too small or large. Helpfully, suggestions can be accessed programmatically through an API.
You’ll find both the IAM Recommender and the Compute Engine Rightsizing Recommender in the Cloud Console, from where you can view and optimize the policy bindings. To opt out of suggestions from either, head to the Recommendation section in the Security & Privacy navigation panel from the Cloud Console.