This chapter will help you to learn about machine learning as a service (MLaaS) by using vRealize Automation. The ML workflow has data cleaning, model selection, feature engineering, model training, and inference. The production of the ML infrastructure is complicated to develop and manage because all ML processes will need to have their hardware and software modified.

We can minimize this complication by automating the provisioning of hardware resources, configuring them along with the operating system and application package, and giving access them to the related IT team. This process customization can be introduced as MLaaS. We will learn how vRealize Automation provides MLaaS with use cases of MLaaS. It will also help in the design and configuration of the blueprint to define the process with workflows in vRealize Automation. We'll also look at load balancer as a service (LBaaS) and how network as a service (NaaS) can remove bottlenecks in hardware-based network architectures.

We will cover the following topics in this chapter:

• VMware approaches for MLaaS and its architecture
•  LBaaS with use cases
•  Transforming network and security services