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AutoML


Alphabet CEO Sundar Pichai has compared the potential impact of artificial intelligence (AI) to the impact of electricity—so it may be no surprise that at Google Cloud, we expect to see increased AI and machine learning (ML) momentum across the spectrum of users and use cases. Some of the momentum is more foundational, such as the hundreds …

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 …

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 …

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 …

Artificial intelligence (AI) and particularly machine learning (ML) continue to advance at breakneck pace. We see it throughout projects and commentaries across the broader technology industry. We see it in the amazing things our customers are doing, from creating friendly robots to aid childhood development, to leveraging data for better manufacturing and distribution, to fostering internal innovation through …

Bringing AI models to a production environment is one of the biggest challenges of AI practitioners. Much of the discussions in the AI/ML space revolve around model development. As shown in this diagram from the canonical Google paper “Hidden Technical Debt in Machine Learning Systems”, the bulk of activities, time and expense in building and …

Are you storing your data in BigQuery and interested in using that data to train and deploy models? Or maybe you’re already building ML workflows in Vertex AI, but looking to do more complex analysis of your model’s predictions? In this post, we’ll show you five integrations between Vertex AI and BigQuery, so you can …

Let’s face it: in the globalized world, which is now more than ever a digital demand world, you need to scale and reach your customers right where they’re at. Translation is a critical piece of that, whether you’re translating a website in multiple languages or releasing a document, a piece of software, or training materials. …

Imagine you’ve done your due diligence for your upcoming trip to the local authority office. You’ve filled out the relevant forms, booked the appointment, found the correct points of contact. But, as much as you’ve prepared, there’s one aspect of your visit you can’t control: you and the clerks don’t speak the same language. In …