Future Cloud Architecture Part III: AI in the Cloud Today, Predictions for Tomorrow
Jun 1, 2023
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The podcast discusses the state of AI in cloud systems, addressing its potential, ethical concerns, integration into cloud provider systems, convergence of meta cloud models and AI enhancements, emerging standards, proprietary systems, and avoiding past mistakes in the cloud market.
The misapplication of AI technology can lead to overspending, higher development costs, and limited or no business value, highlighting the importance of focusing on practical applications with clear business cases.
Cloud providers can leverage AI capabilities to offer more brain-dead simple automation and protect users from making costly mistakes by optimizing resource allocation and ensuring optimal system sizing.
Deep dives
Implications of AI in Cloud Provider Marketplaces
AI and machine learning (ML) have become common offerings in cloud provider marketplaces, providing an accessible and cost-effective solution for businesses. Cloud providers offer their own native AI services, but cross-cloud solutions are preferred for their ability to operate across different providers. This trend benefits cloud providers immensely as they see increased consumption of storage, compute, and AI services. However, the misapplication of AI technology remains a concern. There is a tendency to overemphasize the value of AI without considering its correct application and business viability.
The Need for Pragmatic AI Use
While AI technology is innovative and game-changing, it needs to have a clear business case for effective implementation. Misapplication of AI can lead to overspending, higher development costs, and limited or no business value. It is crucial to focus on the practical applications of AI, such as automating supply chain operations and risk analytics in banking systems. However, confusion about AI's proper use and value persists, and there is a need for better understanding and guidance from cloud providers to ensure successful and valuable AI integration.
AI and Automation in Cloud Environments
Cloud providers have made progress in incorporating AI capabilities into their automation systems, such as auto-scaling and auto-provisioning. AI-powered systems can optimize resource allocation and enable more efficient usage and cost management. However, there is still room for improvement in automating resource allocation and ensuring optimal system sizing. Cloud users often allocate excessive resources, leading to unnecessary costs. By leveraging AI capabilities, cloud providers can offer more brain-dead simple automation to protect users from making costly mistakes. Interoperability and adherence to standards are crucial for the success of meta cloud models and to avoid proprietary lock-ins.
In the final part of Thomas Erl's interview with David Linthicum (best-selling author of "An Insider's Guide to Cloud Computing"), we address the state of Artificial Intelligence (AI) systems being created for and used in clouds. David shares his experience with Cloud AI and reveals how he sees Cloud AI evolving in the future.