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Unlocking the Power of AI with a Real-Time Data Strategy

CIO

To succeed with real-time AI, data ecosystems need to excel at handling fast-moving streams of events, operational data, and machine learning models to leverage insights and automate decision-making. It’s also used to deploy machine learning models, data streaming platforms, and databases. That’s not to say it’ll be easy.

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How to take machine learning from exploration to implementation

O'Reilly Media - Data

Interest in machine learning (ML) has been growing steadily , and many companies and organizations are aware of the potential impact these tools and technologies can have on their underlying operations and processes. Machine Learning in the enterprise". Executive Briefing: Agile for Data Science teams.

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5 questions CIOs must ask after Southwest Airlines’ failure

CIO

Another key question is whether call centers are staffed and have scalable technologies to support a massive influx of calls and communications that often happen during a crisis. Is your organization learning from past failures? How quickly can you realign operations during a crisis?

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Hyperautomation to Unlock Business Efficiency and Agility

Trigent

In today’s fast-paced business landscape, automation is becoming a critical tool for organizations looking to remain agile. Finally, you will better understand why intelligent automation is the key to unlocking business efficiency and agility. Table of Contents Let’s unravel what Hyperautomation is!

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Hyperautomation to Unlock Business Efficiency and Agility

Trigent

In today’s fast-paced business landscape, automation is becoming a critical tool for organizations looking to remain agile. Finally, you will better understand why intelligent automation is the key to unlocking business efficiency and agility. Table of Contents Let’s unravel what Hyperautomation is!

Agile 52
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Key Data Engineer responsibilities

Apiumhub

While data science and data scientists in particular are concerned with exploring data, finding insights in it, and building machine learning algorithms, data engineering cares about making these algorithms work on a production infrastructure and creating data pipelines in general. Who are data engineers?

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AI in Supply Chain: How to Adopt?

Existek

By optimizing the flow of materials, information, and funds, companies can minimize waste, lower production and transportation costs, and ultimately improve their profitability with AI introduction. Many companies manage to minimize waste, reduce emissions, and use sustainable sourcing and transportation methods.

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