Deployment and monitoring – Hands-On Python Deep Learning for the Web

Deployment and monitoring

After a machine learning model is built, it is merged with the other components of an application and is taken into production. This phase is referred to as model deployment. The true performance of the developed ML model is evaluated after it is deployed into real systems. This phase also involves thorough monitoring of the model to figure out the areas where the model is not performing well and which aspects of the model can be improved further. Monitoring is extremely crucial as it provides the means to enhance the model's performance and thereby enhance the performance of the overall application. 

So, that was a kind of a primer of the most important terminologies/concepts required for an ML project.

For a more rigorous study of the basics of ML, you are encouraged to go through these resources: Machine Learning Crash Course by Google (https://developers.google.com/machine-learning/crash-course/) and Python Machine Learning by Sebastian Raschka (https://india.packtpub.com/in/big-data-and-business-intelligence/python-machine-learning).

For easy reference, you may refer to the following diagram as given in the book, Hands-on Transfer Learning with Python (by Dipanjan et. al), which depicts all of the preceding steps pictorially:

Practically, ML has brought about a lot of enhancements across a wide range of sectors and almost none are left to be impacted by it. This book is focused on building intelligent web applications. Therefore, we will start the next section by discussing the web in general and how it has changed since the advent of AI from a before-and-after perspective. Eventually, we will study some big names and how they are facilitating AI for building world-class web applications that are not only intelligent but also solve some real problems.