And there are a lot of things around implementing an AI/ ML project, which include non-technical legal and ethical issues, and technical issues like bias and security.įor more ML/ AI/ Data Science learning materials, please check my previous posts. Heat Stroked When my childhood best friend’s dad, who I’ve had far too many secret fantasies about, walks into the diner where I waitress and hits on me, I have two options: Point out our unfortunate connection and that if he hadn’t been such a workaholic and absent father while his daughter was growing up, he might have recognized me. After deployment, the model serving, monitoring, and maintenance are also critical steps. This includes the steps from the very beginning like collecting data, making data ready for learning, engineering features, training model, evaluating, and finally operationalizing. I guess in the future, many citizen data scientists might use out-of-the-box applications or platforms to leverage AI/ ML in their daily work without even having an understanding of how things are done.īut in reality, at this point, a lot of us as data scientists/ professionals, need to go through a very long and complex process to make sure the value of the business will be derived it.
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