Google announces that the Document AI Custom Extractor, powered by generative AI, is Generally Available (GA), open to all customers, and ready for production use through APIs and the Google Cloud Console. The Custom Extractor, built with Google’s foundation models, helps parse data from structured and unstructured documents quickly and with high accuracy. Read more.
You can now view step level debugging information for each execution from the Google Cloud console or the REST API. This is especially useful for complicated workflows with lots of steps and parallel branches. Read more.
Svelte is a web framework that stands out from the crowd. It shifts a lot of the heavy lifting away from the browser at runtime to the build phase. Svelte's pre-processing approach translates into smaller, faster, and simpler JavaScript code. On top of Svelte, you can use the SvelteKit meta-framework for routing, server side rendering, Node.js support, and more. Read more.
Google announces a solution template that will help you get started with Jupyterhub on GKE. This greatly simplifies the use of z2jh with GKE templates, offering a quick and easy way to set up Jupyterhub by providing a pre-configured GKE cluster, Jupyterhub config, and custom features. Further, Google also added features such as authentication and persistent storage and cut down the complexity for model prototyping and experimentation. Read more.
Google announces a new BigQuery view that leverages the recent FOCUS to help simplify cloud cost management across clouds. FOCUS helps FinOps practitioners perform fundamental FinOps capabilities using a generic set of instructions and unified schema, regardless of the origin of the dataset. FOCUS is a living, breathing specification that is constantly being iterated on and improved by the Working Group, which consists of FinOps practitioners, CSP leaders, Software as a Service (SaaS) providers, and more. Read more.
Google announces a powerful new tool in your arsenal: Cloud Monitoring metrics in BigQuery (now in Preview). With this capability, you can now combine billing data with resource utilization metrics, empowering you to perform detailed analyses in BigQuery. Read more.
BigQuery customers can now create Document AI Custom Extractors, powered by Google's cutting-edge foundation models, which they can customize based on their own documents and metadata. These customized models can then be invoked from BigQuery to extract structured data from documents in a secure, governed manner, using the simplicity and power of SQL. Read more.
Google announces the preview of Vertex AI transcription models in BigQuery. This new capability can make it easy to transcribe speech files and combine them with other structured data to build analytics and AI use cases — all through the simplicity and power of SQL, while providing built-in security and governance. Using Vertex AI capabilities, you can also tune transcription models to your data and use them from BigQuery. Read more.
Dataflow ML enables scalable local and remote inference with batch and streaming pipelines, as well as facilitating data preparation for model training and processing the results of model predictions. Our new Dataflow ML Starter project provides all of the essential scaffolding and boilerplate required to quickly and easily create and launch a Beam pipeline. Read more.
The Google Cloud mobile app is a powerful tool that can help you troubleshoot and manage your cloud applications from anywhere. With the Google Cloud mobile app, you can easily monitor the status and access services such as the Compute Engine, App Engine, Cloud Storage and Cloud SQL. The app is available for iOS and Android devices, and it's free to download. To get started, simply sign in with your Google Cloud credentials. Read more.
Source: https://cloud.google.com/blog/products/gcp