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Automate mortgage document fraud detection using an ML model and business-defined rules with Amazon Fraud Detector: Part 3

AWS Machine Learning Blog

Data must reside in Amazon S3 in an AWS Region supported by the service. It’s highly recommended to run a data profile before you train (use an automated data profiler for Amazon Fraud Detector ). It’s recommended to use at least 3–6 months of data. Two headers are required: EVENT_TIMESTAMP and EVENT_LABEL.

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Monitoring Machine Learning Models in Production

Heartbeat

Data Quality: The accuracy and completeness of data can impact the quality of model predictions, making it crucial to ensure that the monitoring system is processing clean, accurate data. Model Complexity: As machine learning models become more complex, monitoring them in real-time becomes more challenging.

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Top 10 Reasons for Alation with Snowflake: Reduce Risk with Active Data Governance

Alation

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 data profiling helps data scientists and analysts get important data profiling insights. In Summary.

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Data architecture strategy for data quality

IBM Journey to AI blog

Efficiently adopt data platforms and new technologies for effective data management. Apply metadata to contextualize existing and new data to make it searchable and discoverable. Perform data profiling (the process of examining, analyzing and creating summaries of datasets).

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Data Catalog First, Master Data Management Second: Here’s Why

Alation

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. Using the catalog to review data profiles can help discover other potential quality concerns. MDM Model Objects.

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How and When to Use Dataflows in Power BI

phData

Attach a Common Data Model Folder (preview) When you create a Dataflow from a CDM folder, you can establish a connection to a table authored in the Common Data Model (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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Capital One’s data-centric solutions to banking business challenges

Snorkel AI

Model-ready data refers to a feature library. For example, where verified data is present, the latencies are quantified. It enables users to aggregate, compute, and transform data in some scripted way, thereby promoting feature engineering, innovation, and reuse of data. It is essentially a Python library.