In this two-part series, we explore integrating machine learning (ML) predictions into Amazon Aurora database and Amazon SageMaker. Part 1 covered building a customer churn ML model with SageMaker Autopilot and setting up the Aurora ML and SageMaker integration. Additionally, we discussed invoking the SageMaker endpoint from an Aurora cluster in real-time.
This post delves into optimizing Aurora ML for real-time inference at scale. We simulate an OLTP workload, stress the SageMaker endpoint with multiple requests, and automate orchestration using SQL triggers. The aim is to provide business users with up-to-date data and real-time forecasted values without the need for ETL workloads.
By deploying the provided AWS CloudFormation template, users can set up an Aurora DB cluster, SageMaker endpoint, and AWS Cloud9 instance. Following configuration steps, users establish a connection between AWS Cloud9 and the Aurora cluster to enable predictive workflows that respond to INSERT statements in real-time. SQL triggers automatically invoke the SageMaker endpoint for every new insert and store predictions in the Aurora database.
Through stress testing with the aurora-oltp-test.py script, users can assess the solution’s performance under heavy loads. Metrics analysis indicates potential bottlenecks in the SageMaker endpoint due to high CPU utilization in ensemble modeling. Suggestions for optimization include addressing CPU bottlenecks to enhance overall performance.
Lastly, users are advised to delete resources via the AWS CloudFormation console to avoid unnecessary charges. Overall, this post demonstrates how Aurora ML and SageMaker integration can automate real-time inference and empower businesses to leverage ML predictions efficiently. For further insights, readers can explore related blog posts on player retention and matchmaking using Amazon Aurora ML and SageMaker.
The authors, Konrad Semsch and Rodrigo Merino, bring expertise in ML solutions architecture and emerging technologies, helping customers navigate their AI/ML projects on AWS. With a focus on MLOps and end-to-end ML solutions, the authors aim to simplify complex concepts and provide practical solutions for AI/ML projects.
Article Source
https://aws.amazon.com/blogs/database/adding-real-time-ml-predictions-for-your-amazon-aurora-database-part-2/