Remove Efficiency Remove Energy Remove Latency
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Implementing AWS well-architected pillars with automated workflows

Dynatrace

This is a set of best practices and guidelines that help you design and operate reliable, secure, efficient, cost-effective, and sustainable systems in the cloud. The framework comprises six pillars: Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization, and Sustainability.

AWS 305
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How Edge and Industrial IoT Will Converge in 2025: A New Era for Smart Manufacturing

VoltDB

Advances in the Industrial Internet of Things (IIoT) and edge computing have rapidly reshaped the manufacturing landscape, creating more efficient, data-driven, and interconnected factories. This proximity reduces latency and enables real-time decision-making.

IoT 52
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Why growing AI adoption requires an AI observability strategy

Dynatrace

As organizations turn to artificial intelligence for operational efficiency and product innovation in multicloud environments, they have to balance the benefits with skyrocketing costs associated with AI. The good news is AI-augmented applications can make organizations massively more productive and efficient. Use containerization.

Strategy 288
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These 7 Edge Data Challenges Will Test Companies the Most in 2025

VoltDB

By bringing computation closer to the data source, edge-based deployments reduce latency, enhance real-time capabilities, and optimize network bandwidth. As data streams grow in complexity, processing efficiency can decline. Increased latency during peak loads. Balancing efficiency with carbon footprint reduction goals.

IoT 52
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Top 5 AI Use Cases for IIoT: Enhancing Industrial Operations with Real-Time Data

VoltDB

Read on to explore the top five AI use cases for IIoT, and how AI and IIoT, when combined with Volt Active Data, unlock efficiencies, enhance safety, and drive cost savings. Energy Management Challenge: Energy-intensive industries face high utility costs and pressure to reduce their carbon footprints.

Energy 52
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Dynatrace accelerates business transformation with new AI observability solution

Dynatrace

But energy consumption isn’t limited to training models—their usage contributes significantly more. Model observability provides visibility into resource consumption and operation costs, aiding in optimization and ensuring the most efficient use of available resources.

Cache 276
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Boosted race trees for low energy classification

The Morning Paper

Boosted race trees for low energy classification Tzimpragos et al., We don’t talk about energy as often as we probably should on this blog, but it’s certainly true that our data centres and various IT systems consume an awful lot of it. One efficient way of doing that in analog hardware is the use of current-starved inverters.

Energy 52