Simor Consulting

Category: Data Culture

Technical debt in ML systems: a honest accounting
Technical debt in ML systems: a honest accounting
13 Jul, 2026 | 05 Mins read

Google's 2015 paper "Hidden Technical Debt in Machine Learning Systems" described a problem that has only gotten worse in the decade since. The paper's central observation was that the model itself is

Why I stopped chasing the latest AI framework
Why I stopped chasing the latest AI framework
29 Jun, 2026 | 04 Mins read

In 2023, I rewrote a data pipeline three times because the framework landscape kept shifting. First it was built on LangChain. Then the team wanted to switch to LlamaIndex because it handled retrieval

The invisible infrastructure: why data plumbing matters more than models
The invisible infrastructure: why data plumbing matters more than models
15 Jun, 2026 | 05 Mins read

A Fortune 500 company hired a team of twelve machine learning engineers and tasked them with building a predictive maintenance system for their manufacturing floor. The ML team spent four months evalu

Building a data-driven culture: lessons from 50 engagements
Building a data-driven culture: lessons from 50 engagements
13 May, 2026 | 05 Mins read

The phrase "data-driven culture" has been emptied of meaning by overuse. It appears in every strategy deck, every job posting, every conference talk. Everyone claims to want it. Almost no one can desc

The case for AI skepticism in your data strategy
The case for AI skepticism in your data strategy
27 Apr, 2026 | 04 Mins read

I was in a strategy session where a VP of Data told the room that generative AI would "eliminate the need for data analysts within two years." The room nodded. Budget was reallocated. Three analyst po

DataOps: Creating Culture and Processes for Reliable Data
DataOps: Creating Culture and Processes for Reliable Data
01 Jun, 2024 | 03 Mins read

# DataOps: Creating Culture and Processes for Reliable Data Data quality issues cascade downstream. DataOps applies DevOps principles to data workflows: automation, collaboration, and continuous impr