Enterprise ML architecture pattern overview
Building an enterprise ML platform on AWS starts with creating different environments to enable different data science and operations functions. The following diagram shows the core environments that normally make up an enterprise ML platform. From an isolation perspective, in the context of the AWS cloud, each environment in the following diagram is a separate AWS account:
Figure 9.1 – Enterprise ML architecture environments
As we discussed in Chapter 8, Building a Data Science Environment Using AWS ML Services, data scientists use the data science environment for experimentation, model building, and tuning. Once these experiments are completed, the data scientists commit their work to the proper code and data repositories. The next step is to train and tune the ML models in a controlled and automated environment using the algorithms, data, and training scripts that were created by the data scientists. This...
