Serverless deployment for your Amazon SageMaker Canvas models

Deploying machine learning (ML) models into production can often be a complex and resource-intensive task, especially for customers without deep ML and DevOps expertise. Amazon SageMaker Canvas simplifies model building by offering a no-code interface, so you can create highly accurate ML models using your existing data sources and without writing a single line of code. But building a model is only half the journey; deploying it efficiently and cost-effectively is just as crucial. Amazon SageMaker Serverless Inference is designed for workloads with variable traffic patterns and idle periods. It automatically provisions and scales infrastructure based on demand, alleviating the need to manage servers or pre-configure capacity.

In this post, we walk through how to take an ML model built in SageMaker Canvas and deploy it using SageMaker Serverless Inference. This solution can help you go from model creation to production-ready predictions quickly, efficiently, and without managing any infrastructure.

Solution ...