Achieve high performance and cost-effective model deployment

To maximize your ML investments, high performance and cost-effective techniques, including real-time, asynchronous, and batch, are needed to scale model deployments.

In this session, discuss the different inference options available in Amazon SageMaker such as multi-container endpoints, inference pipelines, multi-model endpoints, frameworks such as TensorFlow and PyTorch, Python-based backend servers, and C++/Go-based backend servers. Pick the best inference option for your ML use case so you can scale to thousands of models across your business.

Click here and download the presentation deck to learn more!