Federated learning for edge devices lets tiny gadgets learn together while keeping data private. This guide covers the basics, tools, and a smart‑home example.
TinyML on-device models let tiny devices run AI locally for privacy, low latency, and low cost. This guide covers tools, design tips, and a sample wake word project.
AI-generated API documentation automates the writing of clear, up‑to‑date docs directly from your code, saving time and boosting developer satisfaction.
AI code review automation turns manual reviews into instant, AI‑driven checks that spot bugs, enforce style, and boost security—helping teams ship faster and safer.
AI network traffic analysis watches every packet, learns normal patterns, and flags threats in real time, cutting false alarms and speeding up response.
AI data privacy compliance leverages machine learning to spot personal data in logs, classify it, and automatically enforce encryption or redaction, turning compliance into a continuous, automated process.
AI cloud cost optimization turns raw usage data into smart recommendations that reduce waste, cut costs by up to 40 %, and automate infrastructure changes.
AI vulnerability management uses machine learning to score software weaknesses, prioritize fixes, and automate patch workflows—making security teams faster and more effective.
AI threat hunting blends anomaly detection, behavioural models, NLP and graph analysis to uncover hidden cyber threats early.
Federated Learning in Healthcare lets hospitals collaborate on AI models while keeping patient data inside each institution. This guide explains the process, benefits, and real‑world use cases.