Posted inAI
From Notebook to Production: Deploying AI Models with FastAPI and Docker
Move beyond Jupyter Notebooks. Discover how to use FastAPI and Docker to build, package, and scale AI models for production environments.
Posted inAI
Beyond Grep: Building Smarter Log Monitoring with AI and Open Source
Move beyond basic grep and regex. This guide shows you how to use Python, the Drain algorithm, and Isolation Forest to build a modern, AI-powered log monitoring system.
Posted inAI
Predicting CPU and RAM Load with Python and Scikit-learn: ML for System Resource Monitoring
A practical guide to building a machine learning model that predicts CPU and RAM load before spikes happen — using Python, psutil, and Scikit-learn. Born from a real 2 AM production incident, this tutorial covers data collection, feature engineering, model training with TimeSeriesSplit, and automated Slack alerting.
Posted inAI
Building AI Recommendation Systems: From Basics to Production Deployment
Learn to build AI recommendation systems from the ground up. This tutorial covers core algorithms like collaborative filtering and content-based methods, then dives into advanced usage with hybrid models and deep learning. Finally, it provides practical tips for deploying and maintaining recommendation systems in production environments.
Posted inAI
Fine-Tuning LLMs for Production: When and How to Master It
When your LLM struggles with specific domain knowledge or consistent output in production, fine-tuning might be the most effective solution. This article explores when and how to apply fine-tuning, focusing on practical steps and modern, efficient techniques like LoRA, to achieve stable and precise results for your AI applications.
Posted inSecurity
Self-Hosting AI Models Securely: A Practical Guide to Data Privacy
Worried about AI data privacy? Explore secure self-hosting options. This step-by-step guide empowers you to deploy AI models on your own servers, ensuring your sensitive data remains private and under your control.
Posted inAI
Prompt Engineering 101: How to Write Effective Prompts That Actually Work
A seven-word system prompt brought down our AI support bot at 3 AM — and the model was working perfectly. This guide covers the prompt engineering fundamentals that prevent production failures: role assignment, output format specification, few-shot examples, chain-of-thought reasoning, and how to build a proper test suite for your prompts.