DevOps Articles

Explore Amazon SageMaker Serverless Inference for Deploying ML Models

Launched at the company’s re:Invent 2021 user conference earlier this month, https://aws.amazon.com/?utm_content=inline-mention‘ https://aws.amazon.com/about-aws/whats-new/2021/12/amazon-sagemaker-serverless-inference/ is a new inference option to deploy machine learning models without configuring and managing the compute infrastructure. The fundamental difference between the other mechanisms and serverless inference is how the compute infrastructure is provisioned, scaled, and managed.

Amazon SageMaker Serverless Inference joins existing deployment mechanisms, including real-time inference, elastic inference, and asynchronous inference.

Luckily, the workflow doesn’t change when switching between the conventional real-time inference endpoint and the new serverless inference endpoint.

In the next part of this series, we will look at the steps involved in publishing a SageMaker serverless inference endpoint for a TensorFlow model.