Summary
In this chapter, you learned how to use a trained model to schedule regular inferences. You learned how to prepare an S3 bucket and how to configure an inference scheduler to monitor said bucket.
This chapter was also important to help you understand the sequence of actions a scheduler runs when it wakes up: this will be critical when you will need to prepare your input data so that it can be detected and used appropriately. You also learned where the results of the inference are stored and how you can interpret them.
In the next chapter, you are going to dive deep into how you can interpret the results of Amazon Lookout for Equipment anomaly detection models and reduce the time it takes to process these insights.