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6 ways generative AI can optimize asset management

IBM Big Data Hub

Every asset manager, regardless of the organization’s size, faces similar mandates: streamline maintenance planning, enhance asset or equipment reliability and optimize workflows to improve quality and productivity. These foundation models, built on large language models, are trained on vast amounts of unstructured and external data.

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How Encored Technologies built serverless event-driven data pipelines with AWS

AWS Big Data

Encored develops machine learning (ML) applications predicting and optimizing various energy-related processes, and their key initiative is to predict the amount of power generated at renewable energy power plants. The amount of data and the number of power plants they need to collect data are rapidly increasing over time.

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The case for predictive AI

CIO Business Intelligence

Predictive AI uses advanced algorithms based on historical data patterns and existing information to forecast outcomes to predict customer preferences and market trends — providing valuable insights for decision-making. It leverages techniques to learn patterns and distributions from existing data and generate new samples.

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The blueprint for a modern data center 

IBM Big Data Hub

Part one of this series examined the dynamic forces behind data center retransformation. Now, we’ll look at designing the modern data center, exploring the role of advanced technologies, such as AI and containerization, in the quest for resiliency and sustainability. AI and containerization are not just buzzwords.

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How Big Data Is Influencing Web Design and What You Can Learn from It

Smart Data Collective

With individuals and their devices constantly connected to the internet, user data flow is changing how companies interact with their customers. Big data has become the lifeblood of small and large businesses alike, and it is influencing every aspect of digital innovation, including web development. What is Big Data?

Big Data 108
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Debunking observability myths – Part 5: You can create an observable system without observability-driven automation

IBM Big Data Hub

The notion that you can create an observable system without observability-driven automation is a myth because it underestimates the vital role observability-driven automation plays in modern IT operations. This information is vital for capacity planning and performance optimization. Why is this a myth?

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Minimizing Supply Chain Disruptions with Advanced Analytics

Cloudera

In summary, predicting future supply chain demands using last year’s data, just doesn’t work. Accurate demand forecasting can’t rely upon last year’s data based upon dated consumer preferences, lifestyle and demand patterns that just don’t exist today – the world has changed. Improve Visibility within Supply Chains.

Analytics 109