Google today launched AI Platform Prediction in general availability, a service that lets developers prep, build, run, and share machine learning models in the cloud. It's based on a Google Kubernetes Engine backend and features an architecture designed for high reliability, flexibility, and low overhead latency. IDC predicts that worldwide spending on cognitive and AI systems will reach $77.6 billion in 2022, up from $24 billion in revenue last year. Gartner agrees: In a recent survey of executives from thousands of businesses worldwide, it found that AI implementation grew a whopping 270% in the past four years and 37% in the past year alone. With AI Platform Prediction, Google adds yet another managed AI service to its portfolio, beating back competitors like Amazon, Microsoft, and IBM.

Google's AI Platform, a cloud-hosted service facilitating machine learning and data science workflows, today gained a new feature in backend models that tap powerful Nvidia graphics chips. In related news, Google debuted a refreshed model training experience that allows users to run a training script on any range of hardware. For the uninitiated, AI Platform enables developers to prep, build, run, and share machine learning models quickly and easily in the cloud. Using built-in data labeling services, they're able to annotate model training images, videos, audio, and text corpora by applying classification, object detection, and entity extraction. A managed Jupyter Notebook service provides support for a slew of machine learning frameworks, including Google's TensorFlow, while a dashboard within the Google Cloud Platform console exposes controls for managing, experimenting with, and deploying models in the cloud or on-premises.