So far, for the various deep learning architectures we've discussed, we have assumed that our input data is static. We have had fixed sets of movie reviews, images, or text to process.
In the real world, whether your organization or project includes data from self-driving cars, IoT sensors, security cameras, or customer-product usage, your data generally changes over time. Therefore, you need a way of integrating this new data so that you can update your models. The structure of the data may change too, and in the case of customer or audience data, there may be new transformations you need to apply to the data. Also, dimensions may be added or removed in order to test whether they impact the quality of your predictions, are no longer relevant, or fall foul of privacy legislation. What do we do in these scenarios?
This is where a tool such...
