Amazon SageMaker Autopilot automatically builds, trains, and tunes the best machine learning (ML) models based on your data, while allowing you to maintain full control and visibility. Autopilot can also deploy trained models to real-time inference endpoints automatically.
If you have workloads with spiky or unpredictable traffic patterns that can tolerate cold starts, then deploying the model to a serverless inference endpoint would be more cost efficient.
Amazon SageMaker Serverless Inference is a purpose-built inference option ideal for workloads with unpredictable traffic patterns and that can tolerate cold starts. Unlike a real-time inference endpoint, which is backed by a long-running compute instance, serverless endpoints provision resources on demand with built-in auto scaling. Serverless endpoints scale automatically based on the number of incoming requests and scale down resources to zero when there are no incoming requests, helping you minimize your costs.
In this post, we show how to deploy Autopilot trained

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