Note: This blog has two authors.
What is context caching? Vertex AI is a Google Cloud machine learning (ML) platform that, among other things, provides access to a collection of generative AI models. This includes the models known under the common name “Gemini models”. When you interact with these models you provide it with all the information about your inquiry. The Gemini models accept information in multiple formats including text, video and audio.
Production systems are being monitored for reliability and performance tracking to say the least. Monitored metrics ‒ a set of measurements that are related to a specific attribute of a system being monitored, are first captured in the executing code of the system and then are ingested to the monitoring backend. The selection of the backend often dictates the methods(s) of ingestion. If you run your workloads on Google Cloud and use self-managed Prometheus server and metric collection, this post will help you to reduce maintenance overhead and some billing costs by utilizing Google Cloud Managed Service for Prometheus for collecting and storing Prometheus metrics.
If you do not care about how much money you spend on your Google Cloud project then this post won’t be interesting to you. If you do, you might find the information below useful.
Google Cloud documentation gives examples of the automated cost control responses. It describes an option to send a notification message to PubSub in addition to the email each time the billing budget alert is triggered.
The specific example that stops the usage shows a Cloud Function (code in NodeJS and Python) that disables the billing account on the project.
This article surveys various health checks in Google Cloud. If you want to learn more, leave your preferences in the feedback form.
Generally speaking, a health check is a function or a method to indicate a general state (a.k.a. health) of the underlying service. Some products elaborate the definition of “general state” to be something particular, such as the ability of the service to respond to requests.
Health checks are an important instrument of service observability.
Google Cloud lets you run Kubernetes in three flavors:
Vanilla is when you do all on your own. This is also the quickest “lift and shift” strategy to migrate your cluster to cloud. Essentially it is just a group of virtual machines that run on Google Compute Engine (GCE). Managed that shifts administration and maintenance tasks from DevOps teams to the cloud service. See Google Kubernetes Engine (GKE) Standard cluster architecture and Autopilot for more details.
You may have seen this notice when opening SLOs Overview in Cloud Console.
This notice announces a recent change in the way of defining services for Cloud Monitoring. Before the change, Cloud Monitoring automatically discovered services that were provisioned in AppEngine, Cloud Run or GKE. These services were automatically populated in the Services Overview dashboard. After the change, all services in the Services Overview dashboard have to be created explicitly. To simplify this task, when defining a new service in UI you are presented with a list of candidates that is built based on the auto-discovered services.
Google Cloud supports service monitoring by defining and tracking SLO of the services based on their metrics that are ingested to Google Cloud. This support greatly simplifies implementing SRE practices for services that are deployed to Google Cloud or that store telemetry data there. To make it even more simple to developers, the service monitoring is able to automatically detect many types of managed services and supports predefined availability and latency SLI definitions for them.
