
Amazon SageMaker enables efficient training and serving of thousands of ML models at scale using SageMaker Processing, training jobs, and multi-model endpoints for cost-effective deployment.


<div><p>This article demonstrates how to use Amazon SageMaker to efficiently train and serve thousands of ML models at scale, using an energy forecasting use case with 1,000 customers.</p><ul><li>SageMaker Processing preprocesses data and creates individual CSV files per customer in S3</li><li>Training jobs use ShardedByS3Key distribution to shard data across instances without duplication</li><li>Checkpoints save individual models to S3, avoiding need to unpack large archives</li><li>Multi-Model Endpoints (MMEs) host multiple models on single endpoint for cost efficiency</li><li>MMEs automatically serve all models in specified S3 paths without redeployment</li><li>Frequently used models cached in memory and disk for low-latency inference</li><li>Solution uses Prophet algorithm for time-series energy consumption forecasting</li></ul><p>SageMaker provides a scalable, cost-effective platform for organizations needing to train and deploy thousands of models simultaneously using managed infrastructure.</p></div>


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