Remove Data Collection Remove Experimentation Remove Modeling Remove Optimization
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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 362
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DataRobot and SAP Partner to Deliver Custom AI Solutions for the Enterprise

DataRobot Blog

Every modern enterprise has a unique set of business data collected as part of their sales, operations, and management processes. So in order to get maximum value from AI, it needs to build machine learning models that are unique to each of its business usecase.

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Of Muffins and Machine Learning Models

Cloudera

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua? The extent to which we can predict how the model will classify an image given a change input (e.g. Model Visibility.

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

CIO Business Intelligence

Data exists in ever larger silos, but real knowledge still resides in employees. But the rise of large language models (LLMs) is starting to make true knowledge management (KM) a reality. These models can extract meaning from digital data at scale and speed beyond the capabilities of human analysts.

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What you need to know about product management for AI

O'Reilly on Data

Instead of writing code with hard-coded algorithms and rules that always behave in a predictable manner, ML engineers collect a large number of examples of input and output pairs and use them as training data for their models. The model is produced by code, but it isn’t code; it’s an artifact of the code and the training data.

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Next Stop – Predicting on Data with Cloudera Machine Learning

Cloudera

The first blog introduced a mock vehicle manufacturing company, The Electric Car Company (ECC) and focused on Data Collection. The second blog dealt with creating and managing Data Enrichment pipelines. The third video in the series highlighted Reporting and Data Visualization. Data Collection – streaming data.

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What is a data scientist? A key data analytics role and a lucrative career

CIO Business Intelligence

As such, a data scientist must have enough business domain expertise to translate company or departmental goals into data-based deliverables such as prediction engines, pattern detection analysis, optimization algorithms, and the like. Semi-structured data falls between the two.