Recently, with the publication of our ML Platforms ebook, and our conference on the topic quickly approaching, I've been talking a lot about scaling machine learning, deep learning and AI in the enterprise.
Every so often, in a conversation about the topic, I'll get a lot of vigorous head nodding, and then, out of the blue, a comment that's squarely focused on compute issues, such as GPUs vs CPUs, distributed training, clusters and the like. It's always a bit disorienting when this happens, as if we were in two different conversations. I mean, I get it. It's satisfying to think you can just throw hardware at a problem like enterprise ML/AI. But "scaling" ML/DL for business (vs in research or academia) is a much broader challenge, and opportunity.
Here are three perhaps under-appreciated aspects of scaling ML/AI in the typical enterprise context:
When I talk to ML/AI, data science, and business leaders, these are the issues I'm hearing that they're concerned about: How do they move more quickly? How do they keep up with the demands of the business? How do they build infrastructure and processes that will allow them to quickly get new teammates productive.
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