Google today announced the preview launch of TensorBoard.dev for sharing TensorBoard machine learning visualizations and TensorFlow Enterprise, a cloud service produced through a collaboration between Google’s TensorFlow and cloud teams.
“You’ll now be able to host and track your ML experiments and share them publicly, no setup required. Simply upload your logs and share the URL so that others can see the experiments and what you’re doing with TensorBoard,” Google VP of engineering Megan Kacholia said onstage today at TensorFlow World in Santa Clara, California.
TensorFlow Enterprise is made to deliver an optimized version of its open source machine learning framework TensorFlow for large businesses. It works with Google’s AI Platform and Kubernetes Engine as well as optimized versions of Deep Learning VMs and Deep Learning Containers. The service is made to supply up to 3x improvements in data reading — the result of changes to how TensorFlow reads and caches files — and up to 3 years of support for security patches and select bug fixes.
“These versions will be supported on Google Cloud, and all patches and bug fixes will be available in the mainline TensorFlow code repository,” Google Cloud AI Platform director of product management Craig Wiley told VentureBeat in an email.
It can also come with direct support from Google Cloud and TensorFlow engineers for businesses to train and deploy AI systems.
“For customers on the cutting edge of AI, we offer a white-glove service. This includes engineer to engineer from both the Google Cloud and TensorFlow team to help with their challenges,” Wiley said.
In related news, last week, Google Cloud Platform introduced updates for AI Platform, a cloud-based software for collaboration between data scientists making AI models such as backend support for Nvidia GPUs to speed workflows. Last month, Google launched TensorFlow 2.0 with tighter Keras integration and eager execution by default.
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