![]() Overview of solutionįor this post, we create an ML model to predict if a transaction is fraudulent or not, given the transaction record. We also show you how to use familiar SQL statements to create and train ML models by combining shared features from the centralized store with local features and use these models to make in-database predictions on new data for use cases such as fraud risk scoring. In a local feature store, you can store sensitive data that can’t be shared across the organization for regulatory and compliance reasons. In this post, we discuss the combined feature store pattern, which allows teams to maintain their own local feature stores using a local Redshift table while still being able to access shared features from the centralized feature store. ![]() As a data scientist building an ML model, you may have access to the identifying information but not the transaction information, and having access to a feature store solves this. For example, an ML model to identify fraudulent financial transactions needs access to both identifying (device type, browser) and transaction (amount, credit or debit, and so on) related features. ![]() However, one challenge in training a production-ready ML model using SageMaker Feature Store is access to a diverse set of features that aren’t always owned and maintained by the team that is building the model. Additionally, you can import existing SageMaker models into Amazon Redshift for in-database inference or remotely invoke a SageMaker endpoint.Īmazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for ML models. Redshift ML currently supports ML algorithms such as XGBoost, multilayer perceptron (MLP), KMEANS, and Linear Learner. We covered in a previous post how you can use data in Amazon Redshift to train models in Amazon SageMaker, a fully managed ML service, and then make predictions within your Redshift data warehouse. Amazon Redshift ML makes it easy for SQL users to create, train, and deploy ML models using SQL commands familiar to many roles such as executives, business analysts, and data analysts. Data analysts and database developers want to use this data to train machine learning (ML) models, which can then be used to generate insights on new data for use cases such as forecasting revenue, predicting customer churn, and detecting anomalies. Amazon Redshift is a fast, petabyte-scale, cloud data warehouse that tens of thousands of customers rely on to power their analytics workloads.
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