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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 363
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DataRobot and SAP Partner to Deliver Custom AI Solutions for the Enterprise

DataRobot Blog

Today, SAP and DataRobot announced a joint partnership to enable customers connect core SAP software, containing mission-critical business data, with the advanced Machine Learning capabilities of DataRobot to make more intelligent business predictions with advanced analytics.

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Health check on Tech: CK Birla Hospitals CIO Mitali Biswas on moving the needle towards innovation

CIO Business Intelligence

In this conversation with Foundry, Mitali discusses the accelerated importance of technology in healthcare, on enabling healthcare providers with data and why her team isn’t afraid of experimentation. The need is for a user-friendly system that captures all the data. Can you tell me about your career path so far?

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Accelerating scope 3 emissions accounting: LLMs to the rescue

IBM Big Data Hub

Some companies attempt to estimate Scope 3 emissions by collecting data from suppliers and manually categorizing data, but progress is hindered by challenges such as large supplier base, depth of supply chains, complex data collection processes and substantial resource requirements.

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eCommerce Brands Use Data Analytics for Conversion Rate Optimization

Smart Data Collective

Collecting Relevant Data for Conversion Rate Optimization Here is some vital data that e-commerce businesses need to collect to improve their conversion rates. Identifying Key Metrics for Conversion Rate Optimization Data collection and analysis are both essential processes for optimizing your conversion rate.

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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.”

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Machine Learning Product Management: Lessons Learned

Domino Data Lab

Pete indicates, in both his November 2018 and Strata London talks, that ML requires a more experimental approach than traditional software engineering. It is more experimental because it is “an approach that involves learning from data instead of programmatically following a set of human rules.”