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ML infrastructure plays a crucial role in the success of a machine learning project. Building and maintaining ML infrastructure can be complex and time-consuming, especially when started from scratch for each project. Obviously, horses for courses; some projects are one-offs and optimising might derail your enthusiasm, and some projects are so large that everything becomes an exception. Most projects, however, are (un)fortunately going to be very similar from a high-level perspective. Each will need some data storage, computation units and means to deploy models. Here are a few short tips.

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