{"id":22709,"date":"2021-03-26T07:25:00","date_gmt":"2021-03-26T07:25:00","guid":{"rendered":"https:\/\/www.experfy.com\/blog\/how-to-deploy-machine-learning-models\/"},"modified":"2023-08-29T07:57:44","modified_gmt":"2023-08-29T07:57:44","slug":"how-to-deploy-machine-learning-models","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/how-to-deploy-machine-learning-models\/","title":{"rendered":"How To Deploy Machine Learning Models"},"content":{"rendered":"\t\t
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A step-by-step overview<\/p>\n

Jupyter notebooks are where machine learning models go to die.<\/p>\n

Wait\u2014 what?<\/p>\n

Unlike what you probably learned in University, building models in a Jupyter notebook or R Studio script is just the very beginning of the process. If your process ends with a model sitting in a notebook, those models almost certainly didn’t create value for your company (some exceptions might be it was only for analytics or you work at Netflix).<\/p>\n

That doesn\u2019t mean that your models aren\u2019t excellent. I\u2019m sure they are. But it probably does mean the people paying you are not super excited by the outcome.<\/p>\n

In general, companies don\u2019t care about state-of-the-art models, they care about machine learning models that actually create value for their customers.<\/p>\n

That process of going from a great model in a notebook to a model that can be integrated into part of a product or accessed by non-technical users is what I want to talk about in this post.<\/p>\n

So, if you want your models to stop dying in notebooks and actually create value, read on.<\/p>\n\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Models are Code and Code Needs to be Trusted<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Your first step will be getting your code to a quality level that you can trust to put into production. I want to highlight 3 important things that should happen in this step: have a good README, write clean functions, and test your code.<\/p>\n

Good READMEs are so important. They should allow anyone (including yourself) to easily see why this code was written, what assumptions were made, what constraints existed, and how to use the code. For machine learning code you should also describe and\/or link to experiments that were run so people can view the process of creating your models. Don\u2019t be afraid to include images, GIFs, and videos in your READMEs. They can help make it much easier to understand.<\/p>\n

This post will not teach you everything you need to know to write clean functions. But some important items to remember:<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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