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Embeddable AI saves time building powerful AI applications

IBM Big Data Hub

Just a few weeks ago, IBM announced an expansion to their embeddable AI software portfolio with the release of three containerized Watson libraries. The new libraries include: IBM Watson Natural Language Processing Library for Embed. IBM Watson Speech-to-Text Library for Embed. Partner solutions using embeddable AI.

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Five open-source AI tools to know

IBM Big Data Hub

When AI algorithms, pre-trained models, and data sets are available for public use and experimentation, creative AI applications emerge as a community of volunteer enthusiasts builds upon existing work and accelerates the development of practical AI solutions.

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MLOps and the evolution of data science

IBM Big Data Hub

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects.

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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. Cloud storage, text mining and social network analytics are vital 21 st century tools. Around 300 B.C.E.,

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

ARMA International

For example, organizations can re-package video libraries, songs, research, and course material for different audiences – customers, researchers, academics, students, and so on; and they can monetize the content via CaaS. Data Analytics. Acting generates new data, causing the cycle to repeat, as shown in Figure 4. Press, Gil.

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Establishing Records Management at Brandeis—The First Eighteen Months

Brandeis Records Manager

Others, like brief and bang-bang, YouTube-style training videos, are planned. I believe that we need less generic “Big Data!” We can’t assume that these pieces are in place at other institutions. Communication Tools and Policy. All of these get the URM word out in one way or another. This approach has served us well to date.