Turbocharge AI Workloads with an AI-Native Data Platform

The explosive growth of AI is placing unprecedented demands on data pipelines, leaving traditional storage solutions struggling to keep up. Bottlenecks and latency issues are hindering the performance of even the most powerful GPUs, stifling innovation and slowing down AI initiatives. This whitepaper reveals how an AI-native data platform can turbocharge your AI workloads and unleash their full potential.

Learn how to eliminate the complexities of legacy data infrastructures and overcome the limitations of outdated storage solutions. Discover the key features and benefits of a truly AI-native data platform, and how it can empower your organisation to achieve faster outcomes, seamless scalability, and unparalleled efficiency.

In this whitepaper, you'll discover how to:

Identify the limitations of traditional storage solutions: Understand the bottlenecks that are holding back your AI workloads.
Embrace the data platform revolution: Learn how a modern data platform can transform your AI infrastructure.
Accelerate your AI initiatives: Achieve faster time-to-insights and drive innovation with an AI-native data platform.
This whitepaper draws on industry expertise and real-world examples to provide a comprehensive guide to optimising your AI data pipeline.

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The AI Project Survival Guide

The desire to incorporate AI into business strategies is widespread, but the path to successful AI project deployment is fraught with challenges. Many projects fail due to a lack of clear vision, inadequate data, and infrastructure limitations. This guide offers a lifeline for navigating these complexities. Gain insights from WEKA's CTO, Shimon Ben David, and learn how to ask the right questions to set your AI projects up for success. In this guide, you'll discover how to:

Building Your AI Business Strategy

This ebook looks at survey findings from 540 enterprise customers across Asia Pacific to shed light on regional and industry AI and generative AI adoption trends, challenges and use cases to help IT, functional and LOB executives make better AI decisions. The report assesses the five dimensions of AI journeys including:

How are micro-optics driving next-generation optical communications?

Next-generation optical interconnects in data centres and artificial intelligence (AI) / large language model (LLM) clusters are facing a number of challenges due to factors such as increased bandwidth demand, the need for ultra-low latency, and high power consumption. The significant growth in data traffic from AI applications requires scalable and energy-efficient solutions, and integrating advanced optical technology with existing infrastructure can be complex. Ensuring reliability and durability under rigorous demands is essential, and balancing performance improvements with cost-effectiveness remains a critical hurdle for widespread adoption.