Google Wednesday launched TensorFlow Enterprise, which promises long-term support for previous versions of TensorFlow on its Google Cloud Platform.
The new product, which also bundles together some existing Google Cloud products for training and deploying AI models, is intended to aid organizations running older versions of TensorFlow.
The product is also designed to help “customers who are working with previous versions of TensorFlow and also those where AI is their business,” said Craig Wiley, director of product management for Google Cloud’s AI Platform.
Open sourced by Google in 2015, TensorFlow is a machine learning (ML) and deep learning framework widely used in the AI industry. TensorFlow Enterprise, available on the Google Cloud Platform (GCP), provides security patches and select bug fixes for certain older versions of TensorFlow for up to three years.
Also, organizations using TensorFlow Enterprise will have access to “engineer-to-engineer assistance from both Google Cloud and TensorFlow teams at Google,” according to an Oct. 30 Google blog post introducing the product.
“Data scientists voraciously download the latest version of TensorFlow because of the steady pace of new, valuable features. They always want to use the latest and greatest,” Forrester Research analyst Mike Gualtieri said.
Yet, he continued, “new versions don’t always work as expected,” so the “”dive-right-in” approach of data scientists is often at conflict with an enterprise’s standards.
Mike GualtieriAnalyst, Forrester Research
“That’s why Google’s TensorFlow Enterprise support of prior versions back to three years will accelerate enterprise adoption,” Gualtieri said. “Data scientists and ML engineers can experiment with the latest and greatest, while enterprise operations professionals can insist on versions that work will continue to be available.”
TensorFlow Enterprise comes bundled with Google Cloud’s Deep Learning VMs, which are preconfigured virtual machine environments for deep learning, as well as the beta version of Google Cloud’s Deep Learning Containers.
To be considered for the initial rollout of TensorFlow Enterprise, however, organizations must have spent $500,000 annually, or commit to spending $500,000 annually on Google Cloud’s Deep Learning VMs, Deep Learning Containers, or AI Platform Training and Prediction products, or some combination of those systems.
Over the past several months, Google has made progress in a campaign to offer more tools on its Google Cloud Platform to train, test, and deploy AI models. In April 2019, the tech giant unveiled the Google Cloud AI Platform, a unified AI development platform that combined a mix of new and rebranded AI development products. At the time, analysts saw the release as a move to attract more enterprise-level customers to Google Cloud.
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