The training, testing, and validation of artificial intelligence algorithms belongs to the every day work of PhD students working in the field of personalised medicine. While a lot of effort goes into data collection and preparation, understanding of the data, and model building, the ultimate goal must be to put those carefully developed models into production. This includes making them available to end users, such as clients and other stakeholders. However, the path from exploratory testing to a deployable product can be tricky and consist of multiple stages and special considerations.
In light of these challenges, this talk will outline the different steps needed to successfully deploy machine learning models. It will cover theoretical considerations on what makes a good product, introduce a selection of deployment options for different platforms, and give technical tips that can be utilised when deploying your own model.
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