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The overwhelmed person’s guide to Google Cloud: week of June 27

July 2, 2024
Richard Seroter

Chief Evangelist, Google Cloud

A weekly curation of the most helpful blogs, exciting new features, and useful events coming out of Google Cloud.

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New and shiny

Three new things to know this week

  • Monitor your models in production better. This new version of Vertex AI Model Monitoring helps you know if your models—running on whatever serving infrastructure—are drifting in their prediction quality. Take a look at the new visualizations!
  • Getting started with Google Cloud keeps getting easier. You can now try Vertex AI without even having a Cloud account/project or even signing in! Just click here. If you want to get rolling with a managed Redis with low cost and low latency, you can now set up single zone instances. And the getting started page in our docs got an overhaul and explains the adoption journey better.
  • Use IAM conditions to control access to BigQuery resources. I like this. Now you have much more fine-grained control over who can access what, when. Apply IAM Conditions by project, folder, or org, and to BigQuery datasets, tables, routines, and models.

Watch this


Apply features to GKE fleets. Check out this short demonstration of the powerful fleet-level feature manager that lets you apply consistent behavior across a set of Kubernetes clusters.

Community cuts

Every week I round up some of my favorite links from builders around the Google Cloud-iverse. Want to see your blog or video in the next issue? Drop Richard a line!

  • What are your options for RAG on Google Cloud? Brett at Zencore does a good job outlining the many ways you can apply a retrieval augmented generation pattern using Google Cloud services.
  • Protecting you when you don’t even realize it. Darren noticed that when he deleted a bunch of org policies, that Google Cloud proactively sent a notice asking him to review these “findings” in Security Command Center. That’s useful!
  • Creating integration tests for BigQuery. Mazlum created a cool new open project (BigTesty) that makes it easier to test SQL queries. Take a look and see if this helps create more trust in your BigQuery deployment updates.

Learn and grow

Three ways to build your cloud muscles this week
  • Building generative AI chatbots within BigQuery. Find lots of code and pointers in this post to help you collect input for your chatbot and then perform contextual inference. Neat walkthrough!
  • Cross-cloud networking, explained. We’ve published some very good documentation to explain network segmentation, service networking, and network security for cross-cloud scenarios. See what you think.
  • Cloud Run is a great place for AI applications. Folks really like Cloud Run because it’s powerful and easy to use. This post looks at the different capabilities Cloud Run offers to help you build the best AI apps possible.
  • A little grounding is a good thing. The ability to ground Vertex AI results with Google Search is such a powerful feature. Read this post to find out how to enable this feature, how the results change, and how to write code with this in mind.

One more thing

Google Cloud and Oracle form a meaningful partnership. Run Oracle databases on Google Cloud infrastructure, use Oracle databases in Google Cloud data centers but managed by Oracle, and connect OCI with Google Cloud without worrying about cross-cloud data transfer charges. Read more here.

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