Programming content focused on building, training, evaluating, and deploying AI and machine learning solutions, including model development, data preparation, and inference workflows. Includes practical IT, automation, and security applications such as predictive analytics, anomaly detection, intelligent assistants, and automated decision-making systems.
Article
Model drift is one of the most common reasons an otherwise healthy ML system starts producing less useful predictions in production. This article explains how to detect drift …
Article
Machine learning can surface unusual patterns in security logs that static rules miss, but only when the data, validation method, and operational controls are chosen carefully. This article …
Tutorial
Learn how to build an anomaly detection model with Python using a practical workflow: define the anomaly problem, prepare data, train a baseline model, validate results, and check …
How-To Guide
Start learning machine learning with a practical workflow: confirm prerequisites, run a small baseline experiment, validate results, and know what to check before production use.
Article
Secure model deployment is not just about protecting the API. It also requires hardening the MLOps pipeline so model artifacts, dependencies, approvals, and runtime controls stay trustworthy from …
Article
AI inference endpoints are often exposed more widely than the models they protect. This article explains how to secure inference pipelines with zero trust access, what to verify …
Article
AI can help surface suspicious network behavior in logs faster than manual rule tuning alone, but only if you know which signals to trust and how to validate …
Tutorial
This tutorial shows how to fine-tune transformer models for text classification with a practical workflow: prepare data, choose a base model, train safely, validate results, and check production …