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In the first post of this three-part series, we presented a solution that demonstrates how you can automate detecting document tampering and fraud at scale using AWS AI and machine learning (ML) services for a mortgage underwriting use case. The following diagram represents each stage in a mortgage document fraud detection pipeline.
User support arrangements Consider the availability and quality of support from the provider or vendor, including documentation, tutorials, forums, customer service, etc. Check out the Kubeflow documentation. Metaflow Metaflow helps data scientists and machine learning engineers build, manage, and deploy data science projects.
Resolvers also provide data format specifications and enable the system to stitch together data from various sources. The API then accesses resource properties—and follows the references between resources—to get the client all the data they need from a single query to the GraphQL server.
In addition, Alation provides a quick preview and sample of the data to help data scientists and analysts with greater data quality insights. Alation’s deep dataprofiling helps data scientists and analysts get important dataprofiling insights. Operationalize data governance at scale.
A data catalog communicates the organization’s data quality policies so people at all levels understand what is required for any data element to be mastered. Documenting rule definitions and corrective actions guide domain owners and stewards in addressing quality issues. MDM Model Objects. MDM Build Objects.
Attach a Common DataModel Folder (preview) When you create a Dataflow from a CDM folder, you can establish a connection to a table authored in the Common DataModel (CDM) format by another application. We suggest prioritizing efficiency in your model designs by ensuring query folding whenever it is feasible.
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