This chapter concludes our journey into creating a deep learning model and deploying it as a web application. Our very last steps included deploying a model that predicts Bitcoin prices built using Keras and using a TensorFlow engine. We finished our work by packaging the application as a Docker container and deploying it so that others can consume the predictions of our model—as well as other applications via its API.
Aside from that work, you have also learned that there is much that can be improved. Our Bitcoin model is only an example of what a model can do (particularly LSTMs). The challenge now is two-fold: how can you make that model perform better as time passes? And, what features can you add to your web application to make your model more accessible? Good luck and keep learning!