Topic 1 Question 6
A company uses Amazon SageMaker for its ML pipeline in a production environment. The company has large input data sizes up to 1 GB and processing times up to 1 hour. The company needs near real-time latency. Which SageMaker inference option meets these requirements?
Real-time inference
Serverless inference
Asynchronous inference
Batch transform
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コメント(13)
- 正解だと思う選択肢: C
Real-Time Inference: Immediate responses for high-traffic, low-latency applications. >> Asynchronous Inference: Near real-time for large payloads and longer processing. Batch Transform: Large-scale, offline processing without real-time needs. Serverless Inference: Low-latency inference for intermittent or unpredictable traffic without managing infrastructure.
👍 9jove2024/11/05 Asynchronous inference
PDF RSS Amazon SageMaker Asynchronous Inference is a capability in SageMaker that queues incoming requests and processes them asynchronously. This option is ideal for requests with large payload sizes (up to 1GB), long processing times (up to one hour), and near real-time latency requirements. Asynchronous Inference enables you to save on costs by autoscaling the instance count to zero when there are no requests to process, so you only pay when your endpoint is processing requests.
👍 3sachin_koenig2024/11/03- 正解だと思う選択肢: C
C is right. Amazon SageMaker Asynchronous Inference is a capability in SageMaker that queues incoming requests and processes them asynchronously. This option is ideal for requests with large payload sizes (up to 1GB), long processing times (up to one hour), and near real-time latency requirements. Asynchronous Inference enables you to save on costs by autoscaling the instance count to zero when there are no requests to process, so you only pay when your endpoint is processing requests.
👍 3wmj2024/12/02
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