Countries across the Asia Pacific region continue to launch programs to develop digital and cloud skills for the future workforce. In Australia and Thailand, Google is working with governments, not-for-profit organizations, and universities to bring development programs to enable the next generation of workers with the right digital skills. Learners at universities, and individuals supported […]

Introduction Companies moving to the cloud and running containers are often looking for elasticity. The ability to scale up or down as needed, means paying only for the resources used. Using automation allows engineers to focus on applications rather than on the infrastructure. These are key features of the cloud native and managed container orchestration […]

Overcoming blockades and potholes that threaten to derail organizational change is key to any IT or security transformation initiative. Many security and risk leaders have made it a priority to adopt Zero Trust access models so they can deliver  better user experiences and strengthen security. Yet before they can even think about change management, they […]

Artificial intelligence (AI) and machine learning (ML) have become an increasingly important enterprise capability, including use cases such as product recommendations, autonomous vehicles, application personalization, and automated conversational platforms. Building and deploying ML models demand high-performance infrastructure. Using NVIDIA GPUs can greatly accelerate the training and inference system. Consequently, monitoring GPU performance metrics to understand […]

Bio-pharma organizations can now leverage quick start tools and setup scripts to begin running scalable workloads in the cloud today.  This capability is a boon for research scientists and organizations in the bio-pharma space, from those developing treatments for diseases to those creating new synthetic biomaterials. Google Cloud’s solutions teams continue to shape products with […]

Typically, data scientists retrain models at regular intervals to keep them fresh and relevant. This practice may turn out to be costly if the model is trained too often or inefficient if the model training isn’t frequent enough to serve the business. Ideally, data scientists prefer to continuously evaluate the models and  intentionally retrain models […]