Google Cloud tools for data storage and processing
Since gathering data is the first major step in an AI/ML project (after establishing the business objectives of the project), we begin our exploration of Google Cloud’s AI/ML-related services by first reviewing the tools for storing and processing data. Figure 3.2 shows the steps in the life cycle that relate to ingesting, storing, and processing data. It should be noted that the Train Model, Evaluate Model, and Monitor Model steps would also usually create outputs that need to be stored somewhere.
Figure 3.11: Ingesting, storing, exploring, and processing data
As can be seen in Figure 3.11, and as we’ve discussed previously, working with data is a very prominent part of any AI/ML project.
Data ingestion
Before we can do anything with data in Google Cloud, we need to get access to the data, and we often want to ingest that data into some kind of storage service on Google Cloud. In this...
