Remove Data Collection Remove Deep Learning Remove Modeling Remove Uncertainty
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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. Machine learning adds uncertainty. Models also become stale and outdated over time.

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Finding the Right Technologies for the Right Scenarios

CIO Business Intelligence

Deeper digital transformation can help companies better deal with uncertainties.”. Bob Chen points out that data ingestion, transmission, storage, and analysis are key steps in digital transformation. Enabling new business models through data. per year over 2022 to 2024. Partnership is a key ingredient to growth.

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An AI Data Platform for All Seasons

Rocket-Powered Data Science

To see this, look no further than Pure Storage , whose core mission is to “ empower innovators by simplifying how people consume and interact with data.” RAG is the essential link between two things: (a) the general large language models (LLMs) available in the market, and (b) a specific organization’s local knowledge base.

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Data Science, Past & Future

Domino Data Lab

data science’s emergence as an interdisciplinary field – from industry, not academia. why data governance, in the context of machine learning is no longer a “dry topic” and how the WSJ’s “global reckoning on data governance” is potentially connected to “premiums on leveraging data science teams for novel business cases”.

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Product Management for AI

Domino Data Lab

It used deep learning to build an automated question answering system and a knowledge base based on that information. It is like the Google knowledge graph with all those smart, intelligent cards and the ability to create your own cards out of your own data. There’s no substitute for that experience.