How To Organize Continuous Delivery of ML/AI Systems: a 10-Stage Maturity Model | Outerbounds
outerbounds.com
How To Organize Continuous Delivery of ML/AI Systems: a 10-Stage Maturity Model | Outerbounds
We found the ML engineering workflow to revolve around the following stages (Figure 1): (1) Data Preparation , which includes scheduled data acquisition, cleaning, labeling, and trans-formation, (2) Experimentation , which includes both data-driven and model-driven changes to increase overall ML performance, and is typically measured by metrics suc... See more
Shreya Shankar • "We Have No Idea How Models will Behave in Production until Production": How Engineers Operationalize Machine Learning.
Culture is the biggest change DevOps has introduced to the software industry. This way of working means that both devs and ops must cross the borders of their disciplines: developers have to learn about the environment in which the software is executed and be able to detect and fix problems in automations and system scripts. Ops and SysAdmins need
... See moreMichael Keeling • Software Architecture Metrics
actual practice at Toyota, its best suppliers, and those organizations that have been high-fidelity learners—also employs all three mechanisms. There’s process simplification by way of linearization. This is not just for assembly line operations but, in the extreme, all processes, such as onboarding of new employees, ramp-up production in new produ
... See moreSteven Spear • Wiring the Winning Organization: Liberating Our Collective Greatness through Slowification, Simplification, and Amplification
Throughout this post, I’ve shared patterns I’ve observed across dozens of AI implementations. The most successful teams aren’t the ones with the most sophisticated tools or the most advanced models – they’re the ones that master the fundamentals of measurement, iteration, and learning.
The core principles are surprisingly simple:
The core principles are surprisingly simple:
- Look at your data.
Hamel Husain • A Field Guide to Rapidly Improving AI Products – Hamel’s Blog
From a software maintenance perspective there is little consensus on how to organize ML projects. It feels like websites before Rails came out: a bunch of random PHP scripts with an unholy mixture of business logic and markup sprinkled throughout.