Summary

In this chapter, you've learned some fundamental concepts in the field of AI and the motivations for ML. You then learned about the many pretrained models that are readily available on GCP for consumption and how you can leverage them for different business use cases. With a good grasp of the AI landscape on GCP, we explored options for building custom models, and you learned about and got hands-on experience with AI Platform and BigQuery ML. In particular, you've learned how you can deploy and serve state-of-the-art models without any ML coding experience. Finally, we briefly discussed MLOps and best practices for productionizing ML models and improving agility and operational efficiency of ML workflows on GCP. These are all essential and yet relatively scarce skills among cloud professionals. And you're now equipped to use AI and ML to incorporate modern and data-driven enterprise architecture practices into your cloud solutions.

In the next chapter, you...