Blog
Writing on AI systems, deployment, and evaluation
These articles focus on how AI systems are designed, tested, deployed, and governed in practice, with an emphasis on trade-offs that matter in real engineering work.
MLOps Systems Blueprint for Reliable AI
Production ML behaves like a three-body problem: code, data, and live behavior all pull in different directions. This guide shows how to turn that motion into a stable, self-correcting delivery loop.
Neural DesignNeural Architectures Decoded: FFNN, RNN, and Transformers
Feedforward nets, RNNs, and transformers are three different ways of teaching machines to notice pattern: layers for shape, recurrence for memory, and attention for selective focus. This guide compares them without losing the math.
Delivery CultureDevOps to MLOps: Building the Shared Delivery Muscle
DevOps taught teams to ship code like a disciplined factory line; MLOps adds a third moving part, data, and suddenly the factory floor shifts under your feet. This guide shows what transfers cleanly and what breaks.
Adaptation MethodsLLM Fine-Tuning: LoRA, QLoRA, DPO, and Mixture-of-Experts
A base LLM is a general instrument; fine-tuning changes how tightly it resonates with your task. This guide maps the adaptation spectrum from prompting to MoE, with the math behind each trade-off.
Distributed PrivacyFederated Learning: Training Models Without Moving Data
Federated learning flips the usual gravity of ML: instead of hauling sensitive data to one warehouse, it sends the model out like a traveling teacher and brings back only the lessons. This guide explains the math and the operational trade-offs.
Data ArchitectureData Warehouse, Data Lake, and Lakehouse: A Visual Architecture Guide
Warehouses, lakes, and lakehouses are really three answers to one question: when should raw data be forced into shape? This guide turns that architectural choice into concrete diagrams and decision rules.
Responsible AIResponsible AI: Safety, Fairness, and Trustworthy Systems
Getting a model to work is only the opening scene; the harder plot begins when it must stay fair, explainable, safe, and accountable under pressure. This guide maps the pillars and practices that keep trust from collapsing.
GovernanceAI Governance and Regulations: From EU AI Act to ISO 42001
AI governance is the moment the story meets law: models leave the lab and enter a world of risk tiers, audits, and named obligations. This guide maps the major frameworks and what they require teams to actually build.
Retrieval SystemsRetrieval-Augmented Generation: Architecture, Evaluation, and Production
RAG gives an LLM a memory it can check instead of bluffing from a frozen past. This guide follows the full pipeline from chunking to evaluation so a prototype can grow into a production system.
Agentic AIAI Agents: From ReAct to Multi-Agent Systems
An agent is what happens when an LLM stops answering once and starts acting repeatedly in the world. This guide traces the control loops, tool use, and guardrails that separate a demo agent from a dependable one.
AI SecuritySecurity & Compliance Standards for AI Systems
AI security begins where ordinary app security stops: the attack can be a dataset, a gradient, or a paragraph that looks harmless. This guide maps that wider threat surface and the controls regulated teams need.
Regulated AIOperating AI in Regulated Environments: HIPAA, GDPR, PCI DSS & Beyond
The moment an AI system touches health, payment, or EU personal data, architecture turns into compliance choreography. This guide translates the major regulations into the engineering artifacts and process controls they demand.
OWASP LLM Top 10OWASP Top 10 for LLM Apps: Real Attacks, Real Fixes
For LLM apps, the attack often arrives as plain language rather than obviously malicious code. This guide walks through the OWASP risks as real failure stories, then shows the concrete controls that stop them.