From the course: Deploying Scalable Machine Learning for Data Science

What you should know

From the course: Deploying Scalable Machine Learning for Data Science

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What you should know

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- [Instructor] Throughout the course I make some assumptions about what listeners are familiar with, so here are some things that I think you should know. First is I assume you're familiar with machine learning. And you don't need to be a machine learning expert and we're not gonna go into a lot of details about specific machine learning algorithms but it helps to understand the typical machine learning development process. I also assume that you're comfortable with programming, or at least reading programming code. Again, there's not a lot of code in this course, but there are a couple of videos where we look at some R and Python code. And probably most importantly I assume that you understand basic operating system principals. Such as you're familiar with processes and CPU utilization, disk utilization, network IO, and terms such as that because we'll be discussing those regarding scalability and monitoring. If you're familiar with these three topics you're ready for learning more about scaling machine learning models.

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