machine learning Archives

Build a Local AI OCR System with GOT-OCR2: No Cloud, No Subscriptions, Just Python

July 18, 2026

Tired of expensive cloud OCR bills? Learn how to build a high-performance, local AI OCR system using Python and GOT-OCR2 to handle complex tables, formulas, and messy documents.

Deploy MLflow on Docker: Track ML Experiments, Manage Model Registry in Real MLOps Pipelines

June 29, 2026

A hands-on guide to deploying MLflow on Docker Compose with PostgreSQL and MinIO, covering experiment tracking, model versioning, and Model Registry stage management for real MLOps pipelines. Built from the experience of a 2 AM production incident caused by missing model lineage.

Machine Learning Directly in PostgreSQL: Train and Predict Using SQL

June 25, 2026

Stop moving data. Learn how to train and deploy XGBoost and Scikit-learn models directly inside PostgreSQL using PostgresML and standard SQL queries.

Running AI Models on AMD GPUs with ROCm: A Production-Ready Guide

June 14, 2026

Ditch the NVIDIA tax. This guide shows you how to set up AMD ROCm 6.1 for high-performance AI inference, featuring real-world benchmarks and setup tips for the Radeon 7900 series.

Auto-labeling with Grounding DINO and SAM: Shrink Dataset Prep from Weeks to Hours

June 7, 2026

Ditch the manual labeling grind. Learn how to chain Grounding DINO and SAM to create a zero-shot auto-labeling pipeline that slashes weeks of work from your CV projects.

Automating Prompt Optimization with DSPy: Declarative LLM Programming That Replaces Manual Prompt Engineering

June 3, 2026

DSPy lets you define LLM pipelines declaratively — specifying inputs and outputs instead of writing prompts by hand — then automatically optimizes them using labeled examples. This guide covers the trade-offs versus manual prompt engineering and walks through a full working implementation with code examples.

How to Convert LLM Models to GGUF Format with llama.cpp Quantization

April 28, 2026

Running large language models locally requires shrinking their file size without destroying quality. This guide walks through the full llama.cpp pipeline: downloading a Hugging Face model, converting it to GGUF format, and quantizing it to Q4_K_M or other levels to fit consumer hardware.