RB

Blog

Deep dives into ML engineering, Audio AI, data science, and production systems — written for engineers who build real things.

All 12 posts

Neuro-Symbolic AI: How to Build Reliable LLM Pipelines That Don't Hallucinate
Featured
LLM & NLP

Neuro-Symbolic AI: How to Build Reliable LLM Pipelines That Don't Hallucinate

Pure LLM generation fails ~60% of the time for structured outputs. Learn how neuro-symbolic architecture combines AI reasoning with deterministic templates to achieve 99%+ production reliability.

Mar 10, 20269 min read
Read
Building Production Speech AI Pipelines: TTS & STT from Scratch to Deployment
Featured
Audio AI

Building Production Speech AI Pipelines: TTS & STT from Scratch to Deployment

A complete engineering guide to building production-grade Text-to-Speech and Speech-to-Text pipelines — covering model selection, MFCC evaluation, latency optimization, and FastAPI deployment.

Mar 1, 202611 min read
Read
FastAPI for ML Model Serving: A Production Engineering Guide
MLOps

FastAPI for ML Model Serving: A Production Engineering Guide

How to build a production-ready ML inference service with FastAPI — covering async workers, model lifecycle management, caching strategies, health checks, and Docker deployment.

FastAPIMLOps+4
Jan 28, 20269 min read
XGBoost + SHAP: Building Explainable ML Models That Work in Production
Machine Learning

XGBoost + SHAP: Building Explainable ML Models That Work in Production

How to train XGBoost models that achieve 90%+ accuracy and explain their predictions using SHAP — with a complete pipeline from feature engineering to production API deployment.

XGBoostSHAP+3
Jan 5, 20268 min read
Python AsyncIO for Data Engineers: Building High-Throughput Production Pipelines
Python

Python AsyncIO for Data Engineers: Building High-Throughput Production Pipelines

A practical guide to async Python for data engineering — covering asyncio patterns, async database queries, concurrent API calls, and the mistakes that kill throughput in production.

PythonAsyncIO+3
Dec 20, 20258 min read
Time-Series Analysis for Financial Data: A Quantitative Engineering Approach
Data Science

Time-Series Analysis for Financial Data: A Quantitative Engineering Approach

How to build a rigorous financial analytics pipeline in Python — covering returns vs price stationarity, rolling volatility, regime detection, Sharpe ratio, max drawdown, and vectorized computation.

Time-SeriesQuantitative Finance+3
Dec 5, 20259 min read
Microservices for ML Products: Building Fault-Tolerant AI Systems at Scale
System Design

Microservices for ML Products: Building Fault-Tolerant AI Systems at Scale

How to architect ML-powered products as fault-isolated microservices — covering domain-driven design, circuit breakers, async event streaming with Kafka, and the tradeoffs that matter at 100K+ users.

System DesignMicroservices+4
Nov 20, 202510 min read
RAG in Production: Building Retrieval-Augmented Generation Systems That Actually Work
LLM & NLP

RAG in Production: Building Retrieval-Augmented Generation Systems That Actually Work

A production engineering guide to RAG — covering chunking strategies, embedding models, vector stores, retrieval quality metrics, and the architecture decisions that separate reliable RAG from hallucinating ones.

RAGLLM+4
Nov 5, 202510 min read