Summary

Throughout this first chapter, we've taken the first steps into learning what GCP offers, how it is different from other public cloud providers, and how Google is building on its ubiquitous applications such as Gmail and Google Maps to provide great services to companies via GCP.

We've also discovered that Google's proven experience in AI and ML, developed through the making of products such as Google Photos, also forms part of the services of GCP. Each AI and ML service can address various use cases and different types of users according to their skills and background. For example, most technical users, such as data scientists, can leverage TensorFlow to have great flexibility and control over their developed ML models, while business users can use Google's solutions to solve specific challenges with Document AI and Contact Center AI. The intermediate category is composed of AI and ML building blocks; these services can accelerate the development of new ML use cases or spread the usage of innovative techniques through a company.

One of these building blocks is BigQuery: its extension, BigQueryML, enables the development of ML models by leveraging existing SQL skills. The use of BigQuery ML can bring great benefits to companies that want to democratize ML, enabling a large segment of employees to participate by simplifying the heaviest and most time-consuming activities that usually require the involvement of different stakeholders, skills, and tools.

In the next chapter, we will get hands-on by creating a new Google Cloud project and accessing BigQuery for the first time.