Remove Experimentation Remove Forecasting Remove Metrics Remove Risk
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Bringing an AI Product to Market

O'Reilly on Data

The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded. It sounds simplistic to state that AI product managers should develop and ship products that improve metrics the business cares about. Agreeing on metrics.

Marketing 361
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10 Technical Blogs for Data Scientists to Advance AI/ML Skills

DataRobot Blog

Other organizations are just discovering how to apply AI to accelerate experimentation time frames and find the best models to produce results. Taking a Multi-Tiered Approach to Model Risk Management. Forecast Time Series at Scale with Google BigQuery and DataRobot. Data scientists are in demand: the U.S. Read the blog.

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Expectations vs. reality: A real-world check on generative AI

CIO Business Intelligence

For every optimistic forecast, there’s a caveat against a rush to launch. Pilots can offer value beyond just experimentation, of course. Saving just six minutes of developer time a month is enough to cover the cost, according to Redfin , although there are other metrics like code quality that organizations will want to track as well.

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eCommerce Brands Use Data Analytics for Conversion Rate Optimization

Smart Data Collective

Understanding E-commerce Conversion Rates There are a number of metrics that data-driven e-commerce companies need to focus on. It is a crucial metric that provides priceless information about your website’s ability to transform visitors into paying customers. Some of the most important is conversion rates.

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7 steps for turning shadow IT into a competitive edge

CIO Business Intelligence

Ask IT leaders about their challenges with shadow IT, and most will cite the kinds of security, operational, and integration risks that give shadow IT its bad rep. That’s not to downplay the inherent risks of shadow IT.

IT 125
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Interview with: Sankar Narayanan, Chief Practice Officer at Fractal Analytics

Corinium

Regulations and compliance requirements, especially around pricing, risk selection, etc., It is also important to have a strong test and learn culture to encourage rapid experimentation. How can advanced analytics be used to improve the accuracy of forecasting? In addition, the traditional challenges remain.

Insurance 250
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3 AI Trends from the Big Data & AI Toronto Conference

DataRobot Blog

The DataRobot expo booth at the 2022 conference showcased our AI Cloud platform with industry-specific demonstrations including Anti-Money Laundering for Financial Services , Predictive Maintenance for Manufacturing and Sales Forecasting for Retail. Today, his team is using open-source packages without a standardized AI platform.