Exam AWS Certified AI Practitioner AIF-C01 topic 1 question 6 discussion

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.

Real-Time Inference Latency Low, immediate Payload Size Typically smaller Processing Time Typically short Asynchronous Inference Latency Delayed, can be in minutes Payload Size Can be large, up to 1GB Processing Time Can be long, up to 15 minutes Since the requirement is near realtime upto 1 hour, and payload size upto 1 GB, Option C is appropriate selection

Asynchronous inference can process data size of 1 GB

Asynchronous inference processes requests in the background, returning a response ID and allowing the client to check for results later, while real-time inference delivers redictions with minimal delay, suitable for interactive applications

Explicação:A inferência assíncrona do Amazon SageMaker é ideal quando: Os dados de entrada são grandes (por exemplo, até 1 GB),O tempo de processamento Pode ser longo (até 1 hora por solicitação),E você ainda precisa de respostas com latência razoável, mas não exige resposta instantânea como em APIs síncronas. Ela permite que você envie a solicitação, continue processando outras tarefas e recupere o resultado quando estiver pronto, o que evita timeouts comuns em inferência síncrona.

Here the keyword is near “real time latency “ Asynchronous inference. queues incoming requests and processes them asynchronously. This option is ideal for requests with large payload sizes (up to 1GB), long processing times (up toAsynchronous Inference one hour), and near real-time latency requirements

Amazon SageMaker Asynchronous Inference is a capability in SageMaker AI 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

Real-time inference in Amazon SageMaker is designed for low-latency, high-throughput applications where predictions need to be made immediately after data is processed. Since the company requires near real-time latency for their ML pipeline and has processing times of up to 1 hour and input sizes up to 1 GB, real-time inference is the most suitable option. With real-time inference, you can deploy your trained models as an API endpoint and get predictions on demand, ensuring low latency. This is ideal for situations where you need immediate responses after submitting the data.

The company requires near real-time latency, which means the model needs to respond quickly to inference requests. Real-time inference in Amazon SageMaker is designed for low-latency applications where predictions are needed in milliseconds to seconds. C. Asynchronous inference – Useful for large requests that take minutes or hours to process, but it is not real-time.

Asynchronous inference in Amazon SageMaker is ideal when you have large input data sizes (like the 1 GB mentioned) and relatively long processing times (like up to 1 hour). While real-time inference typically offers lower latency, it may struggle with large datasets or complex models that require more processing time. In contrast, asynchronous inference can handle large inputs and longer processing times without needing immediate results. It processes the data and provides the results later, which might be acceptable if your requirement for near real-time latency can be slightly relaxed (for instance, if results can be retrieved within minutes rather than immediately).

C: Asynchronous inference Explanation: Asynchronous inference in Amazon SageMaker is specifically designed to handle large payloads (up to 1 GB) and long processing times (up to 1 hour). It decouples request submission from processing, allowing the client to submit a request and receive a response later when the inference is complete. This makes it suitable for use cases where real-time responses are not strictly required, but near real-time results are needed.

Whenever "near real-time latency" - asynchronous inference

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.

I think answer A is the correct one as the customer wants to have real-time inference, right?

Key word "real-time latency"

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.

Amazon SageMaker Asynchronous Inference would be the appropriate option. Here’s why: • Handles Large Payloads: Asynchronous Inference is designed to handle large input payloads (up to several GBs) that are typically not suited for real-time, low-latency processing. • Long Processing Times: It supports inference requests that can take minutes to hours to complete, making it ideal for models that require significant processing time. • Near Real-Time Response: While it does not provide millisecond-level latency like real-time endpoints, it offers a more scalable and efficient solution for near real-time use cases where the response time can range from seconds to minutes.