In a recent discussion at Pure Storage's Accelerate event, Par Botes highlighted the importance of data quality in successful AI implementations. He emphasized that enterprises must capture, organize, prepare, and align their data correctly, as incomplete or inappropriate data can hinder AI effectiveness. Addressing challenges in feeding GPUs with data, Botes noted that while high-end solutions are evolving, many enterprises still face skill gaps and require sophisticated systems to manage data effectively. Continuous adaptation of models is crucial as insights and data quality improve.
As your data improves, as your insights change, your data has to change with it. Thus, your model has to evolve with it. This becomes a continuous process.
It's hard to create systems that solve problems using AI without having a really good way of organising data, capturing data, then preparing it and aligning it.
The GPUs are incredibly powerful, and they drive a tremendous amount of bandwidth. It's hard to feed GPUs with data at the pace we consume it.
For a regular enterprise type of company, these are new types of systems and new types of skills they have to implement.
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