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Google Cloud


This is part one of a two-part series with practical tips to start your AI/ML journey. Machine learning (ML) and artificial intelligence (AI) are creating more personalized and easier digital experiences for constituents. According to recent studies, 92% of U.S. citizens1 report that improved digital services would positively impact their view of government. At the same …

Digital channels and on-demand banking have led customers to expect instant and helpful access to managing their finances, with minimal friction. Google Cloud built Contact Center AI (CCAI) and DialogFlow CX to help banks and other enterprises deliver these services, replacing phone trees or sometimes confusing digital menus with intelligent chatbots that let customers interact conversationally, just as they …

When you build a machine learning product, you need to consider at least two MLOps scenarios. First of all, the model could be replaced later, as breakthrough algorithms are introduced in academia or industry. Secondly, the model itself has to evolve with the data in the changing world. We can handle both scenarios with the services provided …

​​Imagine what could be possible if we had a dataset that could automatically and in near real-time show you how the Earth has changed week over week, month over month, year over year. We would get a birds eye view of recent events like floods, fires, snowstorms from days ago and be able to identify seasonal changes on the surface of the …

Earlier this year, we shared details about our collaboration with USAA, a leading provider of insurance and financial services to U.S. military members and veterans, who leveraged AutoML models to accelerate the claims process. Boasting a peak 28% improvement relative to baseline models, the automated solution USAA and Google Cloud produced can predict labor costs and …

Many users within an organization play important roles in the machine learning (ML) lifecycle. There are product managers, who can simply type natural language queries to pull necessary insights from BigQuery, data scientists, who work on different aspects of building and validating models, and ML engineers, who are responsible for keeping the models working well …

At Google we use technologies like machine learning (ML) to build more useful products — from filtering out email spam, to keeping maps up to date, to offering more relevant search results. Chrome is no exception: We use ML to make web images more accessible to people who are blind or have low vision, and we also generate real-time captions for online …

NVIDIA GPU-powered instances on Google Cloud provide an optimal platform for organizations to develop their AI applications on the latest hardware and software stack, then seamlessly deploy those applications at scale in production. Simplifying Workflows to Speedup AI Developments NVIDIA recently announced the One Click Deploy feature on the NVIDIA NGC catalog, the hub for GPU-optimized …

Here are a few situations that you’ve probably encountered: Financial accounts: Companies need to validate the identity of individuals. When creating a customer account, you need to present a government-issued ID for manual validation. Transportation networks: To handle subscriptions, operators often manage fleets of custom identity-like cards. These cards are used for in-person validation, and …

Don’t look now, but Brain Corp operates over 20,000 of its robots in factories, supermarkets, schools and warehouses, taking on time-consuming assignments like cleaning floors, taking inventory, restocking shelves, etc. And BrainOS®, the AI software platform that powers these autonomous mobile robots, doesn’t just run in the robots themselves — it runs in the cloud. Specifically, Google …