Speed meets scale: Load testing SageMakerAI endpoints with Observe.AI’s testing tool

This post is cowritten with Aashraya Sachdeva from Observe.ai.

You can use Amazon SageMaker to build, train and deploy machine learning (ML) models, including large language models (LLMs) and other foundation models (FMs). This helps you significantly reduce the time required for a range of generative AI and ML development tasks. An AI/ML development cycle typically involves data pre-processing, model development, training, testing and deployment lifecycles. By using SageMaker, your data science and ML engineering teams can offload a lot of the undifferentiated heavy lifting involved with model development.

While SageMaker can help teams offload a lot of heavy lifting, engineering teams still have to use manual steps to implement and fine-tune related services that are part of inference pipelines, such as queues and databases. In addition, teams have to test multiple GPU instance types to find the right balance between performance and cost.

Observe.ai provides a ...