MLOps and AIML operations services that move models from laptop to production. See how the AIM-IT framework closes the lab-to-live gap.
MLOps and AIML Operations Services: From Model to Production
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AI
AI and ML Data Analytics Solutions: Use Cases, Stack and Outcomes
A practitioner guide to AI and ML data analytics: real use cases, the modern MLOps stack, the challenges that stall projects, and
AIM-IT: The 90-Day Framework From AI Assessment to Production
A 90-day AI implementation framework: assess, innovate, model, implement and track, with the named artifact each phase produces.
An AI Risk Register Template for Mid-Market Teams
A copy-ready AI risk register template with 18 fields, each mapped to a NIST AI RMF function and subcategory, plus a scoring scale, owner roles and a review cadence a mid-market team can run.
Don’t Automate Chaos: A Field Guide to AI Process Improvement That Actually Sticks
The short...
Databricks Lakebase: When Postgres on the Lakehouse Makes Sense
Databricks Lakebase puts managed Postgres next to your lakehouse tables. Here is when that beats running a separate OLTP database, and when it does not.
Model Risk Management for AI After SR 26-2
SR 26-2 replaced SR 11-7 on 17 April 2026 with a narrower, risk-based model risk management guidance. Here is what it covers, what its footnote on generative AI actually says, and how to produce the evidence on a lakehouse.
The Physics of Getting Poorer: Why Your Human Capital is Being Repriced in the AI Economy
Hard work and elite cognitive talent are being demonetized. Between 7% annual currency debasement and AI repricing labor, the “physics” of your career has shifted. This article explores the Gunpowder Problem of technological sovereignty and introduces the AIM-IT framework to help you reposition into arenas where human judgment creates a structural advantage.
AI Content Generation: From Caves to Code
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