AWS Machine Learning Blog

Category: Artificial Intelligence

­­­­How Sleepme uses Amazon SageMaker for automated temperature control to maximize sleep quality in real time

This is a guest post co-written with Trey Robinson, CTO at Sleepme Inc. Sleepme is an industry leader in sleep temperature management and monitoring products, including an Internet of Things (IoT) enabled sleep tracking sensor suite equipped with heart rate, respiration rate, bed and ambient temperature, humidity, and pressure sensors. Sleepme offers a smart mattress […]

Publish predictive dashboards in Amazon QuickSight using ML predictions from Amazon SageMaker Canvas

Understanding business trends, customer behavior, sales revenue, increase in demand, and buyer propensity all start with data. Exploring, analyzing, interpreting, and finding trends in data is essential for businesses to achieve successful outcomes. Business analysts play a pivotal role in facilitating data-driven business decisions through activities such as the visualization of business metrics and the […]

Announcing new Jupyter contributions by AWS to democratize generative AI and scale ML workloads

Project Jupyter is a multi-stakeholder, open-source project that builds applications, open standards, and tools for data science, machine learning (ML), and computational science. The Jupyter Notebook, first released in 2011, has become a de facto standard tool used by millions of users worldwide across every possible academic, research, and industry sector. Jupyter enables users to […]

Schedule your notebooks from any JupyterLab environment using the Amazon SageMaker JupyterLab extension

Jupyter notebooks are highly favored by data scientists for their ability to interactively process data, build ML models, and test these models by making inferences on data. However, there are scenarios in which data scientists may prefer to transition from interactive development on notebooks to batch jobs. Examples of such use cases include scaling up […]

Announcing provisioned concurrency for Amazon SageMaker Serverless Inference

Amazon SageMaker Serverless Inference allows you to serve model inference requests in real time without having to explicitly provision compute instances or configure scaling policies to handle traffic variations. You can let AWS handle the undifferentiated heavy lifting of managing the underlying infrastructure and save costs in the process. A Serverless Inference endpoint spins up […]

Accelerate protein structure prediction with the ESMFold language model on Amazon SageMaker

Proteins drive many biological processes, such as enzyme activity, molecular transport, and cellular support. The three-dimensional structure of a protein provides insight into its function and how it interacts with other biomolecules. Experimental methods to determine protein structure, such as X-ray crystallography and NMR spectroscopy, are expensive and time-consuming. In contrast, recently-developed computational methods can […]

Transform, analyze, and discover insights from unstructured healthcare data using Amazon HealthLake

Healthcare data is complex and siloed, and exists in various formats. An estimated 80% of data within organizations is considered to be unstructured or “dark” data that is locked inside text, emails, PDFs, and scanned documents. This data is difficult to interpret or analyze programmatically and limits how organizations can derive insights from it and […]

Host ML models on Amazon SageMaker using Triton: Python backend

Amazon SageMaker provides a number of options for users who are looking for a solution to host their machine learning (ML) models. Of these options, one of the key features that SageMaker provides is real-time inference. Real-time inference workloads can have varying levels of requirements and service level agreements (SLAs) in terms of latency and […]

Securing MLflow in AWS: Fine-grained access control with AWS native services

With Amazon SageMaker, you can manage the whole end-to-end machine learning (ML) lifecycle. It offers many native capabilities to help manage ML workflows aspects, such as experiment tracking, and model governance via the model registry. This post provides a solution tailored to customers that are already using MLflow, an open-source platform for managing ML workflows. […]

Host ML models on Amazon SageMaker using Triton: TensorRT models

Sometimes it can be very beneficial to use tools such as compilers that can modify and compile your models for optimal inference performance. In this post, we explore TensorRT and how to use it with Amazon SageMaker inference using NVIDIA Triton Inference Server. We explore how TensorRT works and how to host and optimize these […]