Summary
In this chapter, we focused on scaling your ability to run, develop, and distribute models using a Databricks environment. We also looked at integrating an Apache Spark flow into our batch-inference workflows to handle scenarios where we have access to large datasets.
We concluded the chapter with two approaches to scale hyperparameter optimization and application programming interface (API) serving with scalability, using the NVIDIA RAPIDS framework and the Ray distributed framework.
In the next chapter and in further sections of the book, we will focus on the observability and performance monitoring of ML models.
