Serverless - Google Cloud Community
Hi, We have been using Imagick with Google App Engine with runtime php81 for some time. I changed the runtime and decided to explore Cloud Run for my project. When trying to link my billing account to Cloud Run, it asked me to request quota increase. I'm now running processes on Cloud Run and even tried spinning up a VM called from a Flask app on Cloud Run. Additionally, I'm configuring API Gateway to expose endpoints for my Firebase app using OAuth2.
Integrating with GCP Services
I know I can mount Google Cloud Storage to Cloud Run, but I'm curious if I can also mount block storage, known as Persistent Disk on GCP. I am also working on a project that involves integrating with an API endpoint for user registration. Currently, I'm exploring cost optimization for a media API running on Cloud Run and configuring Cloud Run functions in a shared VPC host project.
Managed Prometheus for Metrics
I have reviewed Cloud Run documentation for setting up managed Prometheus for metrics collection. I fetch data from a source daily using cloud functions where the source system needs to whitelist certain static IP addresses. Updating to the latest version of Google Cloud CLI has also been a recent task.
Error Troubleshooting
While working on a Cloud Build deployment for a Cloud Run Function, I encountered an error that prevented the deployment. I also reported an unusual issue in my GCP project and faced challenges with mapping custom domains in Cloud Run services. Additionally, connecting Gen1 Cloud Functions to Cloud SQL and using external storage in Cloud Run services have been recent areas of exploration.
Deployment Challenges
Issues like Cloud Run service deployments failing and Cloud Functions encountering errors have prompted troubleshooting and resolution steps. Furthermore, scaling Cloud Run services to zero and dealing with CPU allocation have been ongoing concerns.
Application Hosting and Error Handling
Having a Node.js backend hosted on App Engine and facing intermittent issues with deployment and functionality has required attention. Similarly, providing signed URLs for file uploads to Cloud Storage has led to error investigations. Resolving errors in Spring Cloud Function deployments and handling pipeline agent errors in Cloud Run have been recent technical challenges.
Service Optimization and Best Practices
Optimizing service configuration, ensuring smooth deployments, and resolving errors are essential for maintaining efficient operations on Google Cloud Platform. Understanding best practices for MLOps and VM selection to build scalable ML pipelines is crucial for successful project implementation.
Embedding Content with Looker
Learn how to use Looker’s signed iFrame URLs to embed content, such as Dashboards, Looks, or Explores, within customer-facing applications. Discover comprehensive insights on MLOps best practices with Intelligent Product Essentials, offering a structured framework for building robust ML pipelines on Google Cloud Platform. Explore VM selection guidelines through four simple steps to swiftly set up and run on Compute Engine.
