Future Trends

Google Cloud to Provide Energy Department With Cloud, Work Productivity Tools

Google Cloud

Google Cloud to Provide Energy Department With Cloud, Work Productivity Tools

Google Cloud has entered into a five-year agreement to provide the Department of Energy with a range of cloud tools, including its cloud platform and workspace productivity tools. 

The agreement is expected to make it easier for the department's over 100K employees and contractors to access Google's tools, effectively scaling research efforts and driving innovation. 

“Our work with Google Cloud is helping us reduce the friction and pivot to innovation. With this agreement, we’re helping our labs focus on solving problems and get to a place where they can pick the compute they need to get their jobs done,” said Energy Chief Information Officer Rocky Campione.

The department operates dozens of field offices and operates 17 laboratories, including the Lawrence Livermore, Sandia, Los Alamos, Fermi and Oak Ridge national laboratories.

Their agreement covers the whole suite of the Google Cloud Platform services, including Google Cloud Storage, BigQuery, AutoML, Cloud GPUs and TPUs, Google Kubernetes Engine and TensorFlow. 

Energy plans to implement Google Cloud across a variety of uses cases, including the use of machine learning models for preventative maintenance and the use.

The department's personnel may now also use Google Professional Services and Chrome Browser Support, in addition to having access to future Google Cloud offerings when they become commercially available.

“The DOE conducts some of the most cutting-edge research in the world. We are both proud and humbled to play a leading role in helping the DOE advance critical work in the energy sector for the betterment of mankind,” said Mike Daniels, Google Cloud's global public sector vice president

Category: Future Trends

Tags: agreement AutoML BigQuery cloud. Google Cloud Platform CloudGPU Future Trends Google Cloud Google Cloud Storage Google Kubernetes Engine Google Workspace machine learning Mike Daniels national laboratories preventative maintenance Rocky Campione TensorFlow