Deploying ML Models as a Service
In the previous chapter, you built a model using RHODS. In this chapter, you will start packaging and deploying your models as a service. You will see that you do not need any application development experience to expose your model. This capability enables your data science teams to be more agile in testing new models and making them available for consumption.
In this chapter, we will cover the following topics.
- Packaging and deploying models as a service
- Autoscaling the deployed models
- Releasing new versions of the model
- Securing the deployed model endpoint
Before we start, please make sure that you have completed the model-building steps and performed the configuration mentioned in the previous chapter. We’ll start by exposing our model as an HTTP service.
