Computational Cost

If your modern data stack isn’t powerful enough for AI, it’s costing you money.

Are you ready for the AI revolution? If your company is using a modern data stack, it might not be enough. The current practice focuses on mature database technologies that are optimized for storage and retrieval, with hardware choices that prioritize storage and bandwidth resources. But these stacks are not equipped to handle the complex transformations required for feature engineering in AI.

For instance, creating clumpiness features involves a five-step process that includes calculating inter-event times, applying a logarithmic transformation, and aggregating numerically transformed values, among others. Such operations demand significant CPU/GPU resources and RAM, which legacy data stacks lack.

The consequences of using an outdated data stack for AI are dire. AI data pipelines fail because of insufficient resources, leading to dropped input features and higher latency in calculations. Some companies move their data to a different environment for transformations, incurring additional costs. To make matters worse, optimizing SQL and caching becomes a higher priority, creating technical debt, and limiting agility.

So, what can you do to prepare for the AI revolution? You need an AI-ready data stack that includes an optimized feature transformation library and supporting hardware with higher RAM and CPU specifications. Don’t wait until it’s too late. Upgrade your data stack now to stay ahead of the curve and unleash the full potential of AI for your business.