Machine learning reference architecture
Here, customer data ingested into the cloud and ML framework decides on the customer loan application.
Figure 14.3: ML architecture in the AWS cloud
- Training workflow:
- Datasets enter the process flow using S3. This data may be raw input data or preprocessed from on-premises datasets.
- Ground Truth is used to build a high-quality training labeled dataset for ML models. If required, the data can use the Ground Truth service to label the data.
- AWS Lambda can be used for data integration, preparation, and cleaning before datasets are passed to SageMaker.
- Data scientists will interface with SageMaker to train and test their models. The Docker images used by SageMaker...