Rocket-Powered Data Science

FEBRUARY 15, 2023

Those F’s are: Fragility, Friction, and FUD (Fear, Uncertainty, Doubt). These changes may include requirements drift, data drift, model drift, or concept drift. encouraging and rewarding) a culture of experimentation across the organization. Test early and often. Test and refine the chatbot. Launch the chatbot.

Strategy 289

Strategy Experimentation Uncertainty Machine Learning 289

O'Reilly on Data

MARCH 31, 2020

Machine learning adds uncertainty. The model outputs produced by the same code will vary with changes to things like the size of the training data (number of labeled examples), network training parameters, and training run time. Underneath this uncertainty lies further uncertainty in the development process itself.

Management 332

Management Machine Learning Experimentation Metrics 332

Domino Data Lab

JUNE 23, 2019

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. Yet, this challenge is not insurmountable. for what is and isn’t possible) to address these challenges. Transcript.

Management 110

Management Machine Learning Experimentation Metrics 110

O'Reilly on Data

OCTOBER 13, 2020

In Bringing an AI Product to Market , we distinguished the debugging phase of product development from pre-deployment evaluation and testing. During testing and evaluation, application performance is important, but not critical to success. require not only disclosure, but also monitored testing. Debugging AI Products.

Management 244

Management Metrics Machine Learning Modeling 244