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Building a vision for real-time artificial intelligence

CIO

Data is a key component when it comes to making accurate and timely recommendations and decisions in real time, particularly when organizations try to implement real-time artificial intelligence. Real-time AI involves processing data for making decisions within a given time frame. It isn’t easy.

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Henkel embraces gen AI as enabler and strategic disruptor

CIO

But to achieve Henkel’s digital vision, Nilles would need to attract data scientists, data engineers, and AI experts to an industry they might not otherwise have their eye on. The key account manager or the salesperson is looking at the trade promotion data and it’s giving really great hints.

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How Prompt-Based Development Revolutionizes Machine Learning Workflows

Mentormate

In a previous blog post, we introduced a five-phase framework to plan out Artificial Intelligence (AI) and Machine Learning (ML) initiatives. The Traditional Machine Learning Workflow Initiating a traditional ML project begins with collecting data. Duplicated records are identified and rectified.

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Should you build or buy generative AI?

CIO

Organizations don’t want to fall behind the competition, but they also want to avoid embarrassments like going to court, only to discover the legal precedent cited is made up by a large language model (LLM) prone to generating a plausible rather than factual answer.

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Generative AI – The End of Empty Textboxes

TechEmpower CTO

On a different project, we’d just used a Large Language Model (LLM) - in this case OpenAI’s GPT - to provide users with pre-filled text boxes, with content based on choices they’d previously made. For example, let’s consider Mark. It’s this collaboration between the user and the LLM that drives good results.

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What is a data engineer? An analytics role in high demand

CIO

What is a data engineer? Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines used by data scientists, data-centric applications, and other data consumers. The data engineer role.

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Data engineers vs. data scientists

O'Reilly Media - Data

It’s important to understand the differences between a data engineer and a data scientist. Misunderstanding or not knowing these differences are making teams fail or underperform with big data. I think some of these misconceptions come from the diagrams that are used to describe data scientists and data engineers.