DevOps Articles

New — Introducing Support for Real-Time and Batch Inference in Amazon SageM

https://aws.amazon.com/polly/ To build machine learning models, machine learning engineers need to develop a data transformation pipeline to prepare the data. The process of designing this pipeline is time-consuming and requires a cross-team collaboration between machine learning engineers, data engineers, and data scientists to implement the data preparation pipeline into a production environment.

This feature allows you to reuse the data transformation flow which you created in SageMaker Data Wrangler as a step in Amazon SageMaker inference pipelines.

I select Export to SageMaker Inference Pipeline, and SageMaker Data Wrangler will prepare a fully customized Jupyter notebook to integrate the SageMaker Data Wrangler flow with inference.

With this feature, the inference pipeline uses the SageMaker Data Wrangler flow to transform the data from your inference request into a format that the trained model can use.