https://data-trenches.leandrof.space/tags/machine-learning/Recent content in Machine Learning on Data TrenchesHugo -- gohugo.ioen-usleandrojlfernandes@gmail.com (Leandro Fernandes)leandrojlfernandes@gmail.com (Leandro Fernandes)Sun, 04 Jan 2026 00:00:00 +0000https://data-trenches.leandrof.space/projects/realtime-ml-serving/Sun, 04 Jan 2026 00:00:00 +0000leandrojlfernandes@gmail.com (Leandro Fernandes)https://data-trenches.leandrof.space/projects/realtime-ml-serving/<h2 id="the-challenge">The Challenge</h2> <p>Deploying machine learning models to serve real-time inference requests for client-facing applications with strict latency requirements.</p> <h2 id="the-solution">The Solution</h2> <p>Built and deployed low-latency inference services using modern microservices architecture:</p> <ul> <li>FastAPI-based REST endpoints</li> <li>Docker containerization for consistency</li> <li>Load balancing and auto-scaling</li> <li>Health monitoring and logging</li> </ul> <h3 id="technologies-used">Technologies Used</h3> <ul> <li>FastAPI</li> <li>Docker</li> <li>Machine Learning Deployment</li> <li>API Development</li> </ul> <h2 id="impact">Impact</h2> <ul> <li>Real-time model inference capabilities</li> <li>Low-latency responses for client applications</li> <li>Scalable architecture handling varying load</li> <li>Easy model updates and rollbacks</li> <li>Production-grade reliability</li> </ul> <p>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.</p>https://data-trenches.leandrof.space/projects/nlp-analytics-engine/Thu, 18 Sep 2025 00:00:00 +0000leandrojlfernandes@gmail.com (Leandro Fernandes)https://data-trenches.leandrof.space/projects/nlp-analytics-engine/<h2 id="the-challenge">The Challenge</h2> <p>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.</p> <h2 id="the-solution">The Solution</h2> <p>Designed and implemented an end-to-end pipeline from model training to deployment, including:</p> <ul> <li>Data ingestion and preprocessing pipeline</li> <li>Model training infrastructure</li> <li>Inference serving layer</li> <li>Monitoring and alerting system</li> </ul> <h3 id="technologies-used">Technologies Used</h3> <ul> <li>Python</li> <li>Machine Learning/NLP libraries</li> <li>Distributed processing</li> <li>Containerization (Docker)</li> <li>API development (FastAPI)</li> </ul> <h2 id="impact">Impact</h2> <ul> <li><strong>$700k recurring revenue</strong> generated from the analytics solution</li> <li>Processes semantic data from <strong>25,000+ daily targets</strong></li> <li>Production-grade reliability and performance</li> <li>Real-time analytics delivery to clients</li> </ul> <p>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&rsquo;t be shared publicly given it was trained with confidential data.</p>