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The key to operational AI: Modern data architecture

CIO

From customer service chatbots to marketing teams analyzing call center data, the majority of enterprises—about 90% according to recent data —have begun exploring AI. For companies investing in data science, realizing the return on these investments requires embedding AI deeply into business processes.

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Remember when developers reigned supreme? The market for software coding goes soft

CIO

Job titles like data engineer, machine learning engineer, and AI product manager have supplanted traditional software developers near the top of the heap as companies rush to adopt AI and cybersecurity professionals remain in high demand.

Marketing 152
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The future of data: A 5-pillar approach to modern data management

CIO

This approach is repeatable, minimizes dependence on manual controls, harnesses technology and AI for data management and integrates seamlessly into the digital product development process. Furthermore, generally speaking, data should not be split across multiple databases on different cloud providers to achieve cloud neutrality.

Data 167
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Comprehensive data management for AI: The next-gen data management engine that will drive AI to new heights

CIO

The core of their problem is applying AI technology to the data they already have, whether in the cloud, on their premises, or more likely both. Imagine that you’re a data engineer. The data is spread out across your different storage systems, and you don’t know what is where.

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IT leaders: What’s the gameplan as tech badly outpaces talent?

CIO

Hes seeing the need for professionals who can not only navigate the technology itself, but also manage increasing complexities around its surrounding architectures, data sets, infrastructure, applications, and overall security. We currently have about 10 AI engineers and next year, itll be around 30.

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What is Data Engineering: Explaining Data Pipeline, Data Warehouse, and Data Engineer Role

Altexsoft

If we look at the hierarchy of needs in data science implementations, we’ll see that the next step after gathering your data for analysis is data engineering. This discipline is not to be underestimated, as it enables effective data storing and reliable data flow while taking charge of the infrastructure.

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Data collection and data markets in the age of privacy and machine learning

O'Reilly Media - Data

My goal was to remind the data community about the many interesting opportunities and challenges in data itself. Because large deep learning architectures are quite data hungry, the importance of data has grown even more. Economic value of data. The state of data privacy: Views of key stakeholders.