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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? This post will dive deeper into the nuances of each field.

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10 everyday machine learning use cases

IBM Big Data Hub

Marketers use ML for lead generation, data analytics, online searches and search engine optimization (SEO). ML algorithms and data science are how recommendation engines at sites like Amazon, Netflix and StitchFix make recommendations based on a user’s taste, browsing and shopping cart history.

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How to build a successful AI strategy

IBM Big Data Hub

By giving machines the growing capacity to learn, reason and make decisions, AI is impacting nearly every industry, from manufacturing to hospitality, healthcare and academia. It will also determine the talent the organization needs to develop, attract or retain with relevant skills in data science, machine learning (ML) and AI development.

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How to choose the best AI platform

IBM Big Data Hub

” When observing its potential impact within industry, McKinsey Global Institute estimates that in just the manufacturing sector, emerging technologies that use AI will by 2025 add as much as USD 3.7 Visual modeling: Combine visual data science with open source libraries and notebook-based interfaces on a unified data and AI studio.

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Part 1: OMG! Not another digital transformation article! Is it about understanding the business drivers?

ARMA International

For example, re-packing corporate records can help weave a narrative to promote a brand, enhance corporate social responsibility outreach programs, improve employee loyalty, enhance diversity, equality and inclusion training, and highlight environment, social and governance initiatives. A Very Short History Of Big Data , Forbes [link].

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Foundational models at the edge

IBM Big Data Hub

These include data ingestion, data selection, data pre-processing, FM pre-training, model tuning to one or more downstream tasks, inference serving, and data and AI model governance and lifecycle management—all of which can be described as FMOps.

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A brief history of data and how it helped change the world

Collibra

Yes, the ancient pyramids relied not only on labor and raw materials, but on data collection and analysis. . Data collection is what we do. Today, we think of Big Data as a modern concept. Over time, data accumulated and started to become unwieldy, even useless. . How United by Data connects us.

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