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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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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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GUEST ESSAY: The story behind how DataTribe is helping to seed ‘Cybersecurity Valley’ in Maryland

The Last Watchdog

Within these government labs and agencies, taking place is a groundswell of innovation in deep technology cyber disciplines to the tune of billions of dollars annually over the past three decades. The state counts approximately 109,000 cyber engineers. Two notable examples are Sourcefire, acquired by Cisco for $2.7B

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How we used generative AI to run a generative AI hackathon

Collibra

As the data intelligence company, we’ve long anticipated broad adoption of AI, and Collibrians with data science and machine learning expertise have been working diligently on ways to apply AI/ML. She said some of the first results were more relevant for a manufacturing hackathon than a software hackathon.

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

Collibra

Over time, data accumulated and started to become unwieldy, even useless. . How our ancestors tried to solve the data collection dilemma. government realized the records were so voluminous it would take a decade to analyze the results—and by then, it was time for a new census. . How United by Data connects us.

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