The MLOps landscape in GCP
In this section, we’ll learn what GCP services are related to MLOps. But before that, let’s understand what MLOps is.
Understanding the basic principles of MLOps
When we created the ML model in the previous section, we created some ML code, which included creating features, models, and predictions. I found that much ML content and its discussion on the public internet is about creating and improving ML models. Some examples of typical topics include how to create a Random Forest model, ML regression versus classification, boosting ML accuracy with hyperparameters, and many more.
All of the example topics mentioned previously are part of creating ML code. In reality, ML in a real production system needs a lot more than that. Take a look at the following diagram for the other aspects:
Figure 8.4 – Various ML aspects that ML code is only a small part of
As you can see, it’s logical to have the...
