Google Cloud
Accelerating AI/ML Adoption In The Public Sector: Three Ways To Get Started
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 …
Commerzbank Has Reimagined The Customer Experience With Google Contact Center AI
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 …
MLOps System With AutoML And Pipeline In Vertex AI
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 …
Measuring Climate And Land Changes With AI
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 …
Reimagining AutoML With Google Research: Announcing Vertex AI Tabular Workflows
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 …
Pick Your AI/ML Path On Google Cloud
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 …
Building A More Helpful Browser With Machine Learning
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 …
Building AI In The Cloud: An Easier Way With Google Cloud And NVIDIA
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 …
Automate Identity Document Processing With Document AI
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 …
How A Robotics Startup Switched Clouds And Reduced Its Kubernetes Ops Costs With GKE Autopilot
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 …