From the course: Scalable Data Storage and Processing for AI Workloads

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AI storage considerations

AI storage considerations

From the course: Scalable Data Storage and Processing for AI Workloads

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AI storage considerations

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- [Instructor] We've discussed the requirements of data in the AI workflow and the different storage systems that can be used to meet those requirements. Now, let's discuss some special considerations when you're dealing with AI workloads. There are some common mistakes that you might encounter when storing data for AI. Let's see what they are. The first thing is neglecting data scalability. Now, your AI training data and your model might be small to start off with, but do not underestimate the data growth rate and make sure you always use storage solutions that can scale. The impact of neglecting scalability can lead to storage limitations, requiring costly migrations or restructuring. Another common mistake is to organize your data badly or ignore data organization. If you fail to index label or structure data properly, it makes it difficult to retrieve or analyze that data. This slows down model training and inference due to inefficiencies in data access. Another mistake is…

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