Summary

In this chapter, you learned how to use the core features of RHODS. You learned how to create and manage data science projects, workbenches, storage, and data connections.

You also saw how RHODS does the heavy lifting for hardware and software provisioning for your model development workflow. This includes learning how to take advantage of GPUs through machine pools. This dynamic model development environment enables your team to be more agile and focus on model building instead of managing the libraries.

Finally, you learned how to extend the base images to create a set of environments that is more suited to your needs. There, you learned how to create and use custom notebook images in RHODS. This allows you to further customize and tailor the experiences of your data science team.

In the next chapter, you will learn how to build and package ML models for consumption.