摘要: When it is time for your machine learning pilot programs to graduate and take on the real world, you need to start looking at MLOps.

 

 

A few years ago, everyone was trying to figure out how to get started with artificial intelligence and one of its components, machine learning. But today many organizations have put together pilot programs, identified promising use cases, and even turned around some value for their organizations.

After you've won those initial successes, it's time to expand that value to other use cases and other parts of the organization. But with each of your initial use cases you learned something. You developed some technology that you may want to use again. You identified approaches that may not have worked as well as others. How do you take those lessons and apply it to new projects? How do you ensure that you are not re-inventing the wheel each time you tackle a new data science job?

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"The more you invest in AI, the more you optimize, the more you instrument your business and then use AI to understand it and to predict it, and to accommodate the changes that might happen, then your business becomes more resilient to change and more agile," Shimmin said. "You can't really get that if you're just focusing on one or two spot solutions like just trying to figure out turn rates or sales quarterly numbers."

To achieve that kind of a machine learning practice, you need to solve for three problems: repeatability, scalability, and surety, Shimmin said.

Repeatability means achieving the same results and being replicable. Scalability means you have enough processing power for the job you need to do. Surety means that you can trust the outcome and you can explain how the outcome was achieved. To solve these problems, you need more than just a couple of data scientists and a Jupyter Notebook, according to Shimmin.

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詳見全文: informationweek

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