Best of Google Cloud Platform — December 2024

1. 1
   Article

   freeCodeCamp·1y

   Learn Continuous Integration, Delivery, and Deployment with GitHub Actions, Docker, and Google Cloud Run

   Learn how to set up a Continuous Integration (CI), Continuous Delivery (CD), and Continuous Deployment pipeline using GitHub Actions, Docker, and Google Cloud Run. This guide covers the basics of each concept and provides hands-on steps to build and deploy a Node.js project, including setting up automated tests, creating workflows in GitHub Actions, building and pushing Docker images to Docker Hub, and deploying to both a staging and production environment on Google Cloud Run.

   35
2. 2
   Article

   Towards AI·1y

   How to Deploy ML Models in Production (Flawlessly)

   When deploying machine learning models in production, it is crucial to focus on reliability, scalability, security, and maintainability. Using version control systems helps track different versions of your models, ensuring you can revert to stable versions if issues arise. The post offers insights into achieving reliable deployment for ML models in production environments.

   30
3. 3
   Article

   GitLab·1y

   How to deploy a PHP app using GitLab's Cloud Run integration

   GitLab simplifies the process of deploying PHP applications to Google Cloud Platform using Google Cloud Run. This guide covers setting up a new project in GitLab, creating a service account via Google Cloud integration, configuring Cloud Run, adding new endpoints, and cleaning up resources to avoid charges. The method leverages GitLab CI for seamless integration and deployment.

   23
4. 4
   Article

   Google Cloud·1y

   RAG systems: Best practices to master evaluation for accurate and reliable AI.

   Retrieval-augmented generation (RAG) enhances large language models by connecting them to dynamic and specialized data, but its implementation can be challenging. Thorough evaluation is crucial to avoid 'silent failures' that undermine system reliability. Best practices include establishing a rigorous, automated testing framework, selecting appropriate evaluation metrics, and combining quantitative and qualitative testing. Tools like Ragas and Google Cloud's Vertex AI Gen AI evaluation service can assist in optimizing RAG systems.

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