Michael Hsieh

To maximize your ML investments, high performance and cost-effective techniques are needed to scale model deployments. In this session, learn about the deployment options available in Amazon SageMaker, including optimized infrastructure choices; real-time, asynchronous, and batch inferences; multi-container endpoints; multi-model endpoints; auto scaling; model monitoring; and CI/CD integration...

Patient-level data is generated in different data domains and a variety of modalities, including genomic, medical imaging, and clinical health records. Although machine learning (ML) has been adopted in healthcare and life sciences to improve patient care and outcomes, each data modality provides only a partial view of the patient.

R language and its 16,000+ packages dedicated to statistics and ML are used by statisticians and data scientists in industries such as energy, healthcare, life science, and financial services. Using R, you can run simulations and ML securely and at scale with Amazon SageMaker, while reducing the cost of development by using the fully elastic resources in the cloud.