From the course: AI Data Strategy: Data Procurement and Storage
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Choosing scalable storage solutions for ML-driven AI
From the course: AI Data Strategy: Data Procurement and Storage
Choosing scalable storage solutions for ML-driven AI
- [Instructor] In our last video, we talked about finding the right data and handling it responsibly, but there's a crucial aspect of AI development that often gets over overlooked, data storage. Many teams get so focused on building a sophisticated AI model that they don't give enough thought to how that data will be stored and accessed. Let me illustrate with an example. Imagine a hospital that starts using AI to analyze medical images. Initially, they're processing 100 scans per day with their current storage system coping quite well, but as the AI proves its value, the number of scans increases dramatically. They're now processing thousands of scans every day. Suddenly their system can't keep up. Why? Well, because they didn't plan for this kind of data growth. This theoretical scenario brings to light an important point about data storage for AI. Capacity is just one factor among many. We must think about access speed, processing efficiency, and how to manage growth. Consider…
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