Rocket-Powered Data Science

FEBRUARY 15, 2023

Those F’s are: Fragility, Friction, and FUD (Fear, Uncertainty, Doubt). encouraging and rewarding) a culture of experimentation across the organization. Keep it agile, with short design, develop, test, release, and feedback cycles: keep it lean, and build on incremental changes. Test early and often. Launch the chatbot.

Strategy 290

Strategy Experimentation Uncertainty Machine Learning 290

CIO Business Intelligence

OCTOBER 18, 2023

If anything, 2023 has proved to be a year of reckoning for businesses, and IT leaders in particular, as they attempt to come to grips with the disruptive potential of this technology — just as debates over the best path forward for AI have accelerated and regulatory uncertainty has cast a longer shadow over its outlook in the wake of these events.

Risk 141

Risk Manufacturing Enterprise Technology 141

CIO Business Intelligence

JULY 31, 2023

As vendors add generative AI to their enterprise software offerings, and as employees test out the tech, CIOs must advise their colleagues on the pros and cons of gen AI’s use as well as the potential consequences of banning or limiting it. There’s a lot of uncertainty. People are thinking, ‘How is this going to affect my career?

Strategy 98

Strategy Risk Enterprise Risk Management 98

IBM Big Data Hub

APRIL 18, 2024

While leaders have some reservations about the benefits of current AI, organizations are actively investing in gen AI deployment, significantly increasing budgets, expanding use cases, and transitioning projects from experimentation to production. The AGI would need to handle uncertainty and make decisions with incomplete information.

Cost-Benefit 95

Cost-Benefit Modeling Manufacturing Interactive 95

Occam's Razor

APRIL 8, 2013

Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. Testing out a new feature. If you have access to existing data, take some time to document what the current performance looks like. Identify, hypothesize, test, react. But it is not routine.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

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

DataRobot Blog

OCTOBER 4, 2022

In the last few years, businesses have experienced disruptions and uncertainty on an unprecedented scale. However, hand-coding, testing, evaluating and deploying highly accurate models is a tedious and time-consuming process. Access public documentation to get more technical details about recently released features.

Forecasting 59

Forecasting Sales Data-driven Modeling 59