Everything is created using a script with CloudFormation to automatically build the entire infrastructure for your organization.
With machine learning’s capacity to monitor large datasets beyond what any group of humans could and reliably convert that analysis into valuable insights.
We use custom ML algorithms, implementing our own models as opposed to using pre-built algorithms for model training. This allows us to split the ML optimization and feature engineering and research process out from the ML deployment and provides a greater degree of flexibility and customization.
Everything is created using a script with CloudFormation to automatically build the entire infrastructure for your organization.
For training, we use SageMaker to push our images of algorithms and request the training instance.
We have a monitoring system to tell us when something goes wrong, then we can investigate that issue with and take the necessary corrective actions.
For our ML deployment, everything is orchestrated by AWS Step Functions.
