Docker on Data Trenches

Automating PERM Case Status Monitoring: My EB-3 Green Card Journey

leandrojlfernandes@gmail.com (Leandro Fernandes) — Mon, 09 Mar 2026 12:00:00 -0600

My EB-3 Green Card Journey: Automating PERM Status Checks

The Immigration Process

Going through the U.S. employment-based immigration process is a journey that tests your patience in ways you never imagined. As someone pursuing an EB-3 visa (skilled worker category), I found myself navigating the complex maze of PERM labor certification - the first major step toward permanent residency.

Understanding PERM

PERM (Program Electronic Review Management) is the process where the Department of Labor certifies that there are no qualified U.S. workers available for the position offered to the foreign worker. For most employment-based green cards, including EB-3, this is a mandatory step. The process involves:

Real-time ML Model Serving

leandrojlfernandes@gmail.com (Leandro Fernandes) — Sun, 04 Jan 2026 00:00:00 +0000

The Challenge

Deploying machine learning models to serve real-time inference requests for client-facing applications with strict latency requirements.

The Solution

Built and deployed low-latency inference services using modern microservices architecture:

FastAPI-based REST endpoints
Docker containerization for consistency
Load balancing and auto-scaling
Health monitoring and logging

Technologies Used

FastAPI
Docker
Machine Learning Deployment
API Development

Impact

Real-time model inference capabilities
Low-latency responses for client applications
Scalable architecture handling varying load
Easy model updates and rollbacks
Production-grade reliability

This project showcased the ability to bridge the gap between ML models and production applications, ensuring models could be consumed by real users with minimal latency.

NLP Analytics Engine

leandrojlfernandes@gmail.com (Leandro Fernandes) — Thu, 18 Sep 2025 00:00:00 +0000

The Challenge

Building a production-grade NLP analytics engine capable of processing semantic data from 25,000 daily targets while maintaining high availability and delivering actionable insights to enterprise clients.

The Solution

Designed and implemented an end-to-end pipeline from model training to deployment, including:

Data ingestion and preprocessing pipeline
Model training infrastructure
Inference serving layer
Monitoring and alerting system

Technologies Used

Python
Machine Learning/NLP libraries
Distributed processing
Containerization (Docker)
API development (FastAPI)

Impact

$700k recurring revenue generated from the analytics solution
Processes semantic data from 25,000+ daily targets
Production-grade reliability and performance
Real-time analytics delivery to clients

This project demonstrated the full lifecycle of deploying ML models in production, from data pipeline to client-facing application. The atual output of this project can’t be shared publicly given it was trained with confidential data.