Understand ML infrastructure and MLOps using hands-on examples. Therefore, as ordinary code it has a project structure, a documentation and design principles such as object-oriented programming.Īlso, I will assume that you have already set up your laptop and environment as we talked about on part 1 (if you haven’t feel free to do that now and come back)ĭeep Learning in Production Book □ Learn how to build, train, deploy, scale and maintain deep learning models. Machine learning code is ordinary software and should always be treated as one. One last thing before we start and something that I will probably repeat a lot on this course. But here we will see how we can apply them in deep learning using a hands-on approach (so brace yourselves for some programming). You can imagine that most of them aren’t exclusive for machine learning applications but they can be utilized on all sorts of python projects. These practices mostly refer to how we can write organized, modularized, and extensible python code. Towards that end, we continue our series with a collection of best practices when programming a deep learning model. In part 1 of the Deep Learning in Production course, we defined the goal of this article-series which is to convert a python deep learning notebook into production-ready code that can be used to serve millions of users.
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