[SummerSOC 2025] MLOps with Microservices: A Case Study in the Maritime Domain

This case study describes challenges and lessons learned on building Ocean Guard: a Machine Learning–Enabled System (MLES) for anomaly detection in the maritime domain. First, the paper presents the system’s specification, and architecture. Ocean Guard was designed with a microservices architecture to enable multiple teams to work on the project in parallel. Then, the paper discusses how the developers adapted contract-based design to MLOps for achieving that goal. As a MLES, Ocean Guard employs code, model, and data contracts to establish guidelines between its services. This case study hopes to inspire software engineers, machine learning engineers, and data scientists to leverage similar approaches for their systems.

MLOps: From Data Science to ML-Enabled Systems

[CAIN 2025] A Metrics-Oriented Architectural Model to Characterize Complexity on ML-Enabled Systems

How can the complexity of ML-enabled systems be managed effectively? The goal of this research is to investigate how complexity affects ML-Enabled Systems (MLES). To address this question, this research aims to introduce a metrics-based architectural model to characterize the complexity of MLES. The goal of the metrics is to support architectural decisions, providing a guideline for the inception and evolution of these systems.

The Journey of CodeLab: How University Hackathons Built a Community of Engaged Students

Slides about the paper "The Journey of CodeLab: How University Hackathons Built a Community of Engaged Students", presented during ICGJ24 - The International Conference on Game Jams, Hackathons, and Game Creation Competitions 2024, held at Copenhagen, Denmark.

[PhD Qualifying Exam] A Metrics-Oriented Architectural Model to Characterize Complexity on ML-Enabled Systems

Cloud Native Reactive Microservices Architecture

Lecture about Cloud Native Reactive Microservice Architectures presented in the 12th CS Week at IME-USP

[M.Sc. Defense] An Integrated Implementation of Probabilistic Graphical Models

Presentation for my Master's defense at IME-USP

Continuous Delivery for Machine Learning

Continuous Delivery for Machine Learning is a new set of practices documented by ThoughtWorks about how to bring DevOps principles to the world of AI.

[M.Sc. Pre-Defense] An Integrated Implementation of Probabilistic Graphical Models

Presentation for my Master's defense at IME-USP

Intelligent Systems: Machine Learning at Scale

Intelligent Systems: Machine Learning at Scale

Git Avançado

Palestra sobre técnicas de git avançadas, apresentada na USPCodeLab Summer Tech Talks 2019

11 tips for a successful hackathon

Tips to be successful at hackathons

6 dicas para um pitch de sucesso

Dicas para fazer um pitch de sucesso

Software Engineering vs. Data Science

Slides for the panel "Software Engineering vs. Data Science: Challenges and Patterns" held at CBSoft 2018

Slides + CodeSandbox + Vue

A test for using Slides with embedded CodeSandbox hosting a Vue app

Cloud Native Reactive Microservices Architecture

Lecture about Cloud Native Reactive Microservice Architectures presented in the 10th CS Week at IME-USP

Wireframes MAC0350

Wireframes made for the 2nd EP of MAC0350

ChatBots

Presentation about chatter bots for the course MAC0101 (Integration in the University and in the Career) at IME-USP

Recursive Structures

Presentation about recursive strutures for the course MAC0316 (Fundamental Concepts in Programming Languages) at IME-USP

[M.Sc. Qualifying Exam] An Integrated Implementation of Probabilistic Graphical Models

Presentation for my Master's qualifying exam at IME-USP

ToPS for Developers

Presentation about ToPS (Toolkit of Probabilistic Models of Sequences) for developers.

Biofuels

Presentation about biofuels for my English course

Hacknizer

Presentation about Hacknizer - a platform to organize and host hackathons.

11 dicas para um hackathon de sucesso

Dicas para ser bem sucedido em um hackathon