Architect and build the full machine learning lifecycle with AWS: end-to-end with SageMaker - Part 1

Amazon SageMaker provides a rich set of capabilities that enable data scientists, machine learning engineers, and developers to prepare, build, train, and deploy ML models rapidly and with ease.

The example is Fraud Detection 

To get started, data scientists use an experimental process to explore various data preparation tasks, in some cases engineering features, and eventually settle on a standard way of doing so. Then they embark on a more repeatable and scalable process of automating stages of this process, until the model provides the necessary levels of performance (such as accuracy, F1 score, and precision). Then they package this process in a repeatable, automated, and scalable ML pipeline.

Below is an overall diagram for this 

The general phases of the ML lifecycle are data preparation, train and tune, and deploy and monitor, with inference being when we actually serve the model up with new data for inference.

Below is a very detailed view of ML Ops Life cycle 

the red boxes represent comparatively newer concepts and tasks that are now deemed important to include in, and run in a scalable, operational, and production-oriented (vs. research-oriented) environment.

references:

https://aws.amazon.com/blogs/machine-learning/architect-and-build-the-full-machine-learning-lifecycle-with-amazon-sagemaker/