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As businesses race to unlock the potential of artificial intelligence, they increasingly recognize thatcloudinfrastructure is essential.
Co-founder and Director of Hyve Managed Hosting.
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Why is there a disconnect between cloud and AI readiness?
There are many factors to consider when implementing AI and the most important of which is cost.
Currently, the biggest challenge in adopting AI is the significant upfront investment required to create an AI-ready environment.
As a result, it can be difficult to justify the long-term ROI of AI.
Many organizations are rushing to integrateAI toolsinto their operations without fully considering the infrastructure implications.
To effectively support AI workloads, organizations must prioritize compatibility, scalability,security, and cost-effectiveness.
Key considerations for AI
1.
AI workloads
The specific AI requirements of different companies can vary significantly.
To address these unique demands, bespoke cloud optimization strategies are essential for businesses to consider.
Each AI project has unique resources and high-performance computing requirements.
Consequently, they required a CPU-powered server for software development and testing.
Scalability
Scalability is vital for AI, but it must be balanced with cost-effectiveness.
AI workloads can be unpredictable and fluctuate in size.
Organizations need to carefully consider their scalability demands and choose the infrastructure that is right for them.
Security
Security is critical in AIprojects, especially when outsourcing GPU or processing components.
Sensitive data must be protected to safeguard customer privacy.
The first key aspect of AI security in the cloud involves protecting the AI models and data.
Performance
Certain AI tasks require specific hardware to run most effectively.
These chips are specifically designed to deliver the best performance when processing machine learning workloads.
Understanding the memory requirements of the AI model being trained is also extremely important.
This will allow the creation of a comprehensive brief which is an essential first step.
Companies should also consider which cloud infrastructure best suits the unique needs of each AI project.
MSPs also help companies with their security posture through continuous monitoring to protect cloud environments from threats and vulnerabilities.
Additionally, MSPs can help bridge the skills gap, which remains a common barrier to successful AI adoption.
In fact, 46% of businesses use MSPs to address the ongoing skills shortage.
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The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc.
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