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Labs To Blogs: Learn, Then Run It
Every lab maps to a blog so readers can move from architecture insights to hands-on execution in one click.
Labs To Blog
Fraud Detection Lab
Real-time anomaly detection workflows with transparent model behavior.
Labs To Blog
Agentic Fraud Lab
Rule-generation style agentic flows for modern fraud operations.
Latest From SuperML
View all posts »A curated stream of practical tutorials, production AI notes, and opinionated briefings for builders.
The FSB Said the Quiet Part Loud: AI Must Now Govern AI in Banks
The FSB's 12 sound practices for responsible AI adoption include the most honest regulatory admission yet: human oversight of agentic AI in banking can't scale, so banks need AI to monitor AI.
The Agentic AI Governance Framework Every Enterprise Needs Now
As autonomous AI agents move from demos to production — scheduling meetings, writing code, executing trades — most enterprises have no governance framework built for systems that act, not just predict. Here's what one looks like.
Credit Card Personalization Architecture: The ML Stack That Actually Works
Most banks have the data to personalize credit card offers at scale. Most can't actually do it in production because their ML architecture wasn't designed for real-time decisioning. Here's what the right stack looks like — and why the wrong one loses to Amazon.
Thompson Sampling for Personalization: A Hands-On Tutorial
Learn how Thompson Sampling solves the explore-exploit dilemma for real-time personalization. From Bayesian foundations to a full Python implementation for credit card offer ranking, with production deployment patterns.
FDE Architecture Framework: Build Production ML Systems That Don't Break
Feature-Decision-Execution (FDE) is the layered architecture pattern that separates ML prediction from business logic from system action — the pattern that makes production ML systems maintainable, auditable, and safe to iterate on.
Bank AI Agents Have No Kill Switch — and the Data Proves It
Wolters Kluwer's H1 2026 Banking AI Risk Index found 72% of banks lack kill switches or failure reporting for AI models — the minimum viable governance for agentic AI in production financial systems.
REA Framework & Bank Ontology: A Complete Tutorial
A hands-on tutorial on the REA (Resources, Events, Agents) framework applied to banking ontology — from McCarthy's 1982 origins to building a working OWL ontology with Python, RDFLib, SPARQL queries, and AI/ML integration patterns.
Vector Database Comparison for RAG 2026: Pinecone vs ChromaDB vs Redis vs Weaviate
The vector database you choose for your RAG system determines more than retrieval speed — it shapes your architecture's scalability ceiling, operational complexity, cost model, and what filtering capabilities you have at query time. This is the production comparison for teams choosing in 2026.
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