Summary
In this chapter, you learned techniques to operationalize your models in Amazon Redshift ML.
We discussed how you can create a version of your model. This is important to track the quality of your model over time and to be able to run inferences with different versions.
We then showed you how to optimize your Redshift ML models for accuracy and how you can use the notebooks generated by Amazon SageMaker Autopilot to deepen your understanding of tasks that Autopilot is performing.
We hope you have found this book useful. Our goal when we set out to write this book was to help you gain confidence in these main areas:
- Gaining a better understanding of machine learning and how to use it to solve everyday business problems
- Implementing an end-to-end serverless architecture for ingestion, analytics, and machine learning using Redshift Serverless and Redshift ML
- Creating supervised and unsupervised models, and various techniques to influence your model ...
