Engineering essays from the bench.
War stories, architecture decisions, and the occasional tirade. Written by the engineers who shipped it.
The Modern Data Engineering Stack: From Ingestion to Analytics
The modern data stack: ELT pipelines, dbt, lakehouses, orchestration with Dagster and Airflow, and real-time streaming.
Building Production AI Pipelines with Python
A practical guide to designing, building, and deploying AI/ML pipelines that scale, from data ingestion to model serving with MLOps best practices.
How NLP and Transformers Are Transforming Business Operations
Practical NLP and transformer applications, from document processing and sentiment analysis to knowledge extraction and workflow automation.
Computer Vision for Manufacturing Quality Control
How computer vision automates manufacturing quality control: defect detection, real-time inspection, and predictive quality systems.
Building AI-Powered Customer Support Systems That Actually Work
Building AI customer support that actually resolves issues: architecture, intent recognition, escalation logic, and evaluation.
Responsible AI: Addressing Bias and Fairness in Machine Learning
A working framework for identifying, measuring, and mitigating ML bias: data auditing, fairness metrics, and organizational practice.
Fine-Tuning Large Language Models for Enterprise Use Cases
When and how to fine-tune LLMs: data preparation, LoRA and QLoRA training, evaluation strategy, and production deployment.
Predictive Analytics for Supply Chain Optimization
How predictive analytics optimizes supply chains, from demand forecasting and inventory to logistics routing and risk mitigation.
Feature Stores: The Missing Piece in Your ML Infrastructure
What feature stores are, why production ML needs them, and how to implement one with Feast, Tecton, or a custom build.
Edge AI: Running ML Models on IoT Devices
Deploying ML on edge and IoT devices: model optimization, hardware selection, TensorFlow Lite, and real-world constraints.
