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Google today made its biggest updates in nearly a year for AutoML with the introduction of AutoML Video and AutoML Tables for structured data, two new classes for Google’s suite of services that automate the creation of automated AI systems.
Cloud AutoML for the creation of custom AI models was first introduced in January 2018.
AutoML Tables is a new way for people with no coding experience to create custom AI models using structured tabular datasets. Tables can ingest data from GCP’s BigQuery data warehouse and other storage providers.
“We’re also seeing in most industries things like demand forecasting, all the way through to things like price optimization. All of those are structured data problems and things AutoML Tables can be applied to,” Google Cloud senior director of product management Rajen Sheth told reporters ahead of the release.
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There’s also AutoML Video, which, like the AutoML Video Intelligence service first introduced in late 2017, will be able to use natural language and translation to transcribe conversations, and computer vision to recognize things like scene changes and explicit content. AutoML can be used to create custom classification models that serve customers’ unique needs.
Objection detection for AutoML Video is coming soon, Sheth said.
News announced today amounts to the biggest changes for AutoML since the last Cloud Next took place. Last July, Google introduced AutoML Vision’s drag-and-drop tool for training visual systems in public beta, and introduced AutoML Natural Language and AutoML Translate as well.
Today, AutoML Vision Edge, a subset of AutoML Vision, was introduced to give AI practitioners a way to create low latency image recognition models for remote or on-premises edge deployments. AutoML Vision Edge can utilize edge tensor processing units for faster speeds.
Beta releases introduced today that add to existing AutoML services include AutoML Vision object detection for finding objects in visual imagery, AutoML Natural Language custom entity extraction to find specific keywords and phrases in documents, and AutoML Natural Language custom sentiment analysis for detection of a person’s mood or emotional state.
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