cloudbuild

This repository has Google Cloud solutions that you can deploy with a single click.

Project showing a complete use case with a Cloud Run Service written with a Python module and multiple files. The deployment of service is done with FastApi and Uvicorn.

Distributed Load Testing of REST/gRPC APIs using Locust

Google Cloud Build integration for Slack

Showcase a demo-template for managing a MLOps project by considering code collaboration, repository architecture, continuos integration, continuos development and deployment by implementing Google Cloud services and products.

Batch ETL using Cloud Environment which is GCP by utilizing Cloud Composer + Google Cloud Storage + Dataflow + Cloud Build

Kafka streaming performance tests.

This agent uses Google Open Source Insights (deps.dev) and Vertex AI (Gemini) to resolve complex dependency graphs (including transitive ones) and detect restricted licenses (e.g., AGPL, Commercial) even when metadata is missing.

Quart codebase containing real world examples (CRUD, auth, advanced patterns, etc) that adheres to the RealWorld spec and API.

End-to-End MLOps pipeline on Google Cloud with Vertex AI, Cloud Build, Cloud Deploy, Docker, and Artifact Registry for automated model training and deployment.

Testing Google Cloud Build and Cloud Run

ChocoBot is a Discord bot designed to notify a specific user in a channel about new YouTube comments on a designated channel's videos every 4 hours. If comments are found, it notifies the user and displays a list of comments, specifying the video, the user who made the comment, and the time the comment was made.

Creation of an AI Agent with the pydanticAI framework. Infrastructure in GCP

Cloud-Native Microservices application demonstrating a full DevOps strategy. Features a RAG pipeline (LangChain), containerized with Docker, and deployed via CI/CD to GCP Cloud Run.

using a Pub/Sub push subscription deployed on serverless Google Cloud Run with Cloud Build ci/cd

The goal of this project is to build a salary prediction model, encapsulate it in a Docker container, and set up a continuous integration and deployment (CI/CD) pipeline using Google Cloud Build and Google Cloud Deploy. This setup ensures that the model is automatically built, tested, and deployed to a Kubernetes cluster whenever changes are made.

Este projeto é um desafio que implementa um pipeline ETL (Extract, Transform, Load) que lê arquivos de um bucket no Google Cloud Storage, processa e valida os dados, e os carrega em tabelas do BigQuery. Após o carregamento, o dbt é utilizado para transformar os dados no BigQuery.

Headless agent-driven AI content factory demonstrating ML infrastructure patterns, sandboxed execution, and cloud-ready orchestration.

This is a backend project for the portfolio v2 to track the visitors and love count of the site.