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Charting the course of cloud computing and AI in 2024
By Dan Krantz, Chief Information Officer at Keysight Technologies
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I will continue to disrupt and dominate in 2024, with generative AI (Gen-AI) remaining the poster child.
Organisations will re-evaluate their multi- cloud strategies and adjust workloads between providers to support the demand for compute power that AI is driving. These two mega technology trends will create many challenges and opportunities must consider as they plan for 2024 and beyond. Here are a handful of predictions related to these areas that I expect to come to fruition.
1. The rise of cloud high- performance computing As traditional cloud capabilities mature, workloads typically utilise on-premise supercomputing infrastructure, but the market supercomputing capabilities wrapped in cloud-native characteristics of elasticity, programmable automation, and metered usage, democratising the engineering workloads.
12 April 2024 | Automation
2. Multi-cloud era: The rise of agnostic tools The majority of organisations are predominantly multi-cloud rather than to realise this and are now building better multi-cloud interoperabilities. agreement between Oracle and Microsoft. This leads to organisations needing cloud- agnostic tools for observability, visibility and quality assurance automation.
3. AI is disrupting the cloud market With AI workloads requiring unprecedented GPU memory intensive capacity and next-generation power and cooling, look for potential disruption of the top-three cloud computing Platform. For example, the second- capabilities for GenAI. Additionally, Nvidia could rapidly disrupt the
4. Testing in the brave new world of generative AI Year 2023 has seen a wave of innovations on the back of generative-AI solutions into existing enterprise systems,
[Image: Steve Johnson for Unsplash]
transforming every digital experience – from the employee, to the customer, to the supply chain. accelerate the fundamentals of master data sets. This requires pristinely-clean data harvested specifically for the organisation’s training needs. Secondly, rethink the quality assurance
processes. Having manual testing or static, predictable regression test libraries must transform testing into continuous user experience assurance teams that leverage AI for exploratory, model-based testing injected with chaos engineering to surface unintended anomalies. These user experience assurance teams will need new toolchains and independence from each project and digital product, to continuously validate the desired user experience outcomes.
AI will ultimately determine business develop an AI strategy that supports the business objectives. In addition, they need to foster a culture of cloud adoption and innovation. To realise this, enterprises need trusted and innovative technology partners to help them navigate and thrive in this new, intelligent era.
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