Additional AI/ML Tools, Frameworks, and Considerations

At this point, we have covered all of the major steps and considerations in a typical machine learning (ML) project. Considering that AI/ML is one of the fastest-developing areas of research in the technology industry, new tools, methodologies, and frameworks emerge every day.

In this chapter, we will discuss additional tools and frameworks that are popular in the data science industry that we haven’t covered so far. This includes important topics such as BigQuery ML (BQML), various types of hardware that we can use for AI/ML workloads, and the use of open source libraries and frameworks such as PyTorch, Ray, and Spark MLlib. We will also discuss some tips on how to implement large-scale distributed training on Google Cloud.

At the end of this chapter, I will provide some additional context to help transition the focus of the remainder of this book to Generative AI. This will include diving a bit deeper into some of...