Generative AI in manufacturing refers to using advanced AI algorithms and generative models to optimize various aspects of the production process. This technology enables manufacturers to create innovative product designs, streamline workflows, predict maintenance needs, and boost efficiency in frontline operations. By integrating generative AI, manufacturers can enhance decision-making, collaboration, and data insights, ultimately improving overall performance.

Imagine getting into your car to head to work on a hot day. Your car already knows and sets the temperature, the ambient lighting, and the music you prefer. Not only that, it optimizes your route, and with Level 3 autonomy, it can even drive you there. But what does the automotive industry have to do on the backend in order to achieve this kind of personalization?

Basel 3.1 marks a transformative revision to the Basel 3 Regulatory framework, created in response to the 2008 Financial Crisis. Research shows that nearly 60% of global banks are struggling to fully comply with these new regulations designed to reshape the financial sector. The stringent standards, particularly in critical areas like capital and risk management—specifically for credit and market risk—are further compounded by tight deadlines.

Support for conversational data agents with Gemini and enhancements to BigQuery provide a unified data platform for connecting all your data to AI.

Replicating databases in regulated environments starts with syncing data sources while ensuring the data never leaves the secure perimeter of the cloud or on-premises network.

Rounding numbers in Python is an important concept that simplifies calculations and improves the accuracy of results. It helps you adjust numbers by changing them to the nearest whole number or to a specific number of decimal places. Whether you are working with financial data, scientific measurements, or just everyday calculations, rounding ensures that your results are easy to interpret and use.

After a short break, we’re back with Part 2 of this series on Spring Framework, Confluent Cloud, and the Kotlin language. Many organizations that write applications and microservices for the JVM have chosen Spring Framework, leveraging the many libraries available for features such as REST services, persisting data to a variety of datastores, and integration with messaging. These organizations have existing investments in building, testing, deploying, and monitoring applications using Spring.

It’s a vague, unhelpful message that feels more like a riddle than an error. You know something doesn’t belong, but you're left guessing what that "token" is and why it's unexpected. Let's demystify what’s really going on behind the scenes and get your code running smoothly again. The "SyntaxError: Unexpected token" in JavaScript occurs when the code contains a character or symbol that the JavaScript engine does not expect, often due to a typo or syntax mistake.

Built on BigQuery, Dataplex provides data governance with automated data discovery, curation and management at scale.

BigQuery’s pipe syntax introduces an intuitive, top-down syntax for understanding data transformations, and is used in Cloud Logging Log Analytics.