Google LLC today launched an enterprise version of TensorFlow, the popular open-source artificial intelligence framework it created to run machine learning, deep learning and other statistical and predictive analytics workloads. Common use cases include training algorithms for image recognition and recurrent neural networks, as well as sequence-to-sequence models for machine translation and natural language processing. In a launch at the O'Reilly TensorFlow World conference in Santa Clara, California, Craig Wiley (pictured), director of product management at Google Cloud AI Platform, said the launch of TensorFlow Enterprise was necessary to meet the "higher demands and expectations" of enterprises that need to scale up their machine learning projects. TensorFlow Enterprise customers will be able to take advantage of what Google says is enterprise-grade support, including long-term support for older versions of the framework. Although TensorFlow is updated regularly, not everyone is able to upgrade to the newest releases immediately.

Google has provided its machine learning platform TensorFlow with two new software additions. The first is TensorFlow Enterprise for ML in the cloud, aimed at business customers. Let's take a closer look at their features. TensorFlow Enterprise supports services like AI Platform and Kubernetes Engine for deploying and developing ML applications in the Google Cloud. It is a combination of services and products that are specifically targeted to the demands of enterprise customers.

Google introduced TensorFlow Enterprise, a new collection of machine learning services and products.

Enterprises of the previous decade have transformed from transactional to digital. Today, digital enterprises use machine learning pipelines with humans-in-loop. However, the enterprises of tomorrow will be aiming for end-to-end AI-driven core business solutions, or intelligent enterprises. To address these demands, Google this week announced TensorFlow Enterprise at the ongoing TensorFlow World conference. TensorFlow, one of the most popular machine learning frameworks, was open sourced by Google in 2015.

Google today announced the preview launch of TensorBoard.dev "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.