article thumbnail

Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

If the relationship of $X$ to $Y$ can be approximated as quadratic (or any polynomial), the objective and constraints as linear in $Y$, then there is a way to express the optimization as a quadratically constrained quadratic program (QCQP). However, joint optimization is possible by increasing both $x_1$ and $x_2$ at the same time.

article thumbnail

DataRobot Notebooks: Enhanced Code-First Experience for Rapid AI Experimentation

DataRobot Blog

Data science teams of all sizes need a productive, collaborative method for rapid AI experimentation. By predicting which patients are at risk of readmission before they are discharged, doctors can follow appropriate medical procedures to prevent readmission, optimize costs, and enhance the quality of treatment. Auto-scale compute.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Bringing an AI Product to Market

O'Reilly on Data

Without clarity in metrics, it’s impossible to do meaningful experimentation. AI PMs must ensure that experimentation occurs during three phases of the product lifecycle: Phase 1: Concept During the concept phase, it’s important to determine if it’s even possible for an AI product “ intervention ” to move an upstream business metric.

Marketing 361
article thumbnail

CBRE’s Sandeep Davé on accelerating your AI ambitions

CIO Business Intelligence

Sandeep Davé knows the value of experimentation as well as anyone. CBRE has also used AI to optimize portfolios for several clients, and recently launched a self-service generative AI product that enables employees to interact with CBRE and external data in a conversational manner. And those experiments have paid off.

article thumbnail

Achieving cloud excellence and efficiency with cloud maturity models

IBM Big Data Hub

Optimized: Cloud environments are now working efficiently and every new use case follows the same foundation set forth by the organdization. Cloud security maturity model The optimization of security is paramount for any organization that moves to the cloud. Service ownership is established and distributed to self-sufficient teams.

article thumbnail

MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

ML apps need to be developed through cycles of experimentation: due to the constant exposure to data, we don’t learn the behavior of ML apps through logical reasoning but through empirical observation. but to reference concrete tooling used today in order to ground what could otherwise be a somewhat abstract exercise. Model Operations.

IT 342
article thumbnail

It’s a new dawn of AI-powered knowledge management

CIO Business Intelligence

According to a recently leaked Google memo, “The barrier to entry for training and experimentation has dropped from the total output of a major research organization to one person, an evening, and a beefy laptop.”