AWS Machine Learning Blog

Category: Artificial Intelligence

Unlock insights from your Amazon S3 data with intelligent search

Amazon Kendra is an intelligent search service powered by machine learning (ML). Amazon Kendra reimagines enterprise search for your websites and applications so your employees and customers can easily find the content they’re looking for, even when it’s scattered across multiple locations and content repositories within your organization. Keywords or natural language questions can be […]

Operationalize ML models built in Amazon SageMaker Canvas to production using the Amazon SageMaker Model Registry

You can now register machine learning (ML) models built in Amazon SageMaker Canvas with a single click to the Amazon SageMaker Model Registry, enabling you to operationalize ML models in production. Canvas is a visual interface that enables business analysts to generate accurate ML predictions on their own—without requiring any ML experience or having to […]

Amazon SageMaker with TensorBoard: An overview of a hosted TensorBoard experience

Today, data scientists who are training deep learning models need to identify and remediate model training issues to meet accuracy targets for production deployment, and require a way to utilize standard tools for debugging model training. Among the data scientist community, TensorBoard is a popular toolkit that allows data scientists to visualize and analyze various […]

Reduce Amazon SageMaker inference cost with AWS Graviton

Amazon SageMaker provides a broad selection of machine learning (ML) infrastructure and model deployment options to help meet your ML inference needs. It’s a fully-managed service and integrates with MLOps tools so you can work to scale your model deployment, reduce inference costs, manage models more effectively in production, and reduce operational burden. SageMaker provides […]

­­­­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

April 2024: This post was reviewed and updated for accuracy. 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 […]

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 […]