![]() ![]() After the model is trained, Redshift ML makes it available as a SQL function in your Amazon Redshift data warehouse by compiling it via Amazon SageMaker Neo. Redshift ML handles all the interactions between Amazon Redshift, Amazon S3, and SageMaker, abstracting the steps involved in training and compilation. When you run the SQL command to create the model, Redshift ML securely exports the specified data from Amazon Redshift to Amazon S3 and calls Autopilot to automatically prepare the data, select the appropriate pre-built algorithm, and apply the algorithm for model training. For example, to create a model that predicts customer churn, you can query columns in one or more tables in Amazon Redshift that include the customer profile information and historical account activity as the inputs, and the column showing whether the customer is active or inactive as the output you want to predict. To create an ML model, as a data analyst, you can use a simple SQL query to specify the data in Amazon Redshift you want to use as the data inputs to train your model and the output you want to predict. Redshift ML enables you to use ML with your data in Amazon Redshift without this complexity. This iterative process is time-consuming and prone to errors, and automating the data movement can take weeks or months of custom coding that then needs to be maintained. The following diagram illustrates this workflow. Import predicted columns back into the database.Export prediction input data to Amazon S3.Export training data to Amazon Simple Storage Service (Amazon S3).When the model is deployed and you want to use it with new data for making predictions (also known as inference), you need to repeatedly move the data back and forth between Amazon Redshift and SageMaker through a series of manual and complicated steps: Unfortunately, you often have to learn a new programming language (such as Python or R) to build, train, and deploy ML models in SageMaker. For example, you might need to identify the appropriate ML algorithms in SageMaker or use Amazon SageMaker Autopilot for your use case, then export the data from your data warehouse and prepare the training data to work with these model types.ĭata analysts and database developers are familiar with SQL. You may rely on ML experts to build and train models on your behalf or invest a lot of time into learning new tools and technology to do so yourself. Use Caseĭetect if a customer is going to default a loanĬurrent ways to use ML in your data warehouse The following table shows different types of use cases and algorithms used. The columns that describe customer information and usage are features, and the customer status (active vs. Let’s consider a customer churn prediction use case. You can use supervised training for advanced analytics use cases ranging from forecasting and personalization to customer churn prediction. The following diagram illustrates this architecture. Your training dataset is a table or a query whose attributes or columns comprise features, and targets are extracted from your data warehouse. The inputs used for the ML model are often referred to as features, and the outcomes or results are called targets or labels. As evident in the following diagram, supervised learning is preferred when you have a training dataset and an understanding of how specific input data predicts various business outcomes. With this release, Redshift ML supports supervised learning, which is most commonly used in enterprises for advanced analytics. You may use different ML approaches according to what’s relevant for your business, such as supervised, unsupervised, and reinforcement learning. ML use cases relevant to data warehousing This post shows you how to use familiar SQL statements to create and train ML models from data in Amazon Redshift and use these models to make in-database predictions on new data for use cases such as churn prediction and fraud risk scoring. Redshift ML allows you to use your data in Amazon Redshift with Amazon SageMaker, a fully managed ML service, without requiring you to become an expert in ML. 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.Īmazon Redshift ML makes it easy for SQL users to create, train, and deploy ML models using familiar SQL commands. Tens of thousands of customers use Amazon Redshift to process exabytes of data every day to power their analytics workloads. December 2022: Post was reviewed and updated to announce support of Prediction Probabilities for Classification problems using Amazon Redshift ML.Īmazon Redshift is a fast, petabyte-scale cloud data warehouse data warehouse delivering the best price–performance. ![]()
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