Machine Learning
Machine LearningChurn Prediction: From Logistic Regression to Foundation Models
Customer churn costs businesses billions annually. This technical deep-dive compares statistical methods, gradient boosting, and transformer models like TimesFM 2.5 and Chronos 2 for churn prediction - with benchmarks, architecture diagrams, and implementation insights.
Frederico VicenteDec 202512 min read
Data AnnotationData Labeling: The Overlooked Bottleneck in AI and Machine Learning
Model architectures often get the spotlight, but real-world performance in AI depends heavily on data labeling quality. Learn why annotation workflows, human-in-the-loop systems, and synthetic data strategies are what make an ML model hold up in production.
Frederico VicenteSep 20259 min read
InfrastructureRAM vs VRAM in Mixture of Experts Models: The Hidden Bottleneck in Today's LLMs
Explore how GPU VRAM and system RAM shape the performance of Mixture of Experts models like Qwen3-Next. Learn why memory hierarchy is the real bottleneck in modern LLM deployments and how to optimize infrastructure for speed and scalability.
Frederico VicenteSep 20258 min read
Generative AIRAG vs Fine-Tuning: Why the Best AI Systems Combine Both
Should you choose Retrieval-Augmented Generation (RAG) or fine-tuning to optimize your LLM? The answer is not either-or. Learn how combining RAG with fine-tuning delivers accuracy, adaptability, and cost efficiency in real-world AI systems.
Frederico VicenteSep 20257 min read
Artificial IntelligenceFederated Learning: Privacy-First AI
Train one model across sites that cannot pool their data. How federated learning works, where it fits, and what it costs in accuracy.
Frederico VicenteFeb 20248 min read
Bring us the problem nobody has cracked yet.
We are a small team of senior specialists. We pick the right model and the right layer, and we build the least machinery that does the job. You get a call with an engineer, not a sales deck.