Tag: AIML

Starting with the AWS Neuron 2.18 release, you can now launch Neuron DLAMIs (AWS Deep Learning AMIs) and Neuron DLCs (AWS Deep Learning Containers) with the latest released Neuron packages on the same day as the Neuron SDK release. When a Neuron SDK is released, you’ll now be notified of the support for Neuron DLAMIs […]

Today, we are excited to announce that the first model in the next generation Falcon 2 family, the Falcon 2 11B foundation model (FM) from Technology Innovation Institute (TII), is available through HAQM SageMaker JumpStart to deploy and run inference. Falcon 2 11B is a trained dense decoder model on a 5.5 trillion token dataset […]

In this post, we show how to index information stored in websites and use the intelligent search in HAQM Kendra to search for answers from content stored in internal and external websites. In addition, the ML-powered intelligent search can accurately get answers for your questions from unstructured documents with natural language narrative content, for which keyword search is not very effective.

Modern model pre-training often calls for larger cluster deployment to reduce time and cost. At the server level, such training workloads demand faster compute and increased memory allocation. As models grow to hundreds of billions of parameters, they require a distributed training mechanism that spans multiple nodes (instances). In October 2022, we launched HAQM EC2 […]

Creating robust and reusable machine learning (ML) pipelines can be a complex and time-consuming process. Developers usually test their processing and training scripts locally, but the pipelines themselves are typically tested in the cloud. Creating and running a full pipeline during experimentation adds unwanted overhead and cost to the development lifecycle. In this post, we […]

Predicting common machine failure types is critical in manufacturing industries. Given a set of characteristics of a product that is tied to a given type of failure, you can develop a model that can predict the failure type when you feed those attributes to a machine learning (ML) model. ML can help with insights, but […]

This post is co-written by Shibangi Saha, Data Scientist, and Graciela Kravtzov, Co-Founder and CTO, of Equilibrium Point. Many individuals are experiencing new symptoms of mental illness, such as stress, anxiety, depression, substance use, and post-traumatic stress disorder (PTSD). According to Kaiser Family Foundation, about half of adults (47%) nationwide have reported negative mental health […]

April 2023: This post was reviewed and updated with HAQM SageMaker Canvas’s new features and UI changes. Machine learning (ML) helps organizations increase revenue, drive business growth, and reduce cost by optimizing core business functions across multiple verticals, such as demand forecasting, credit scoring, pricing, predicting customer churn, identifying next best offers, predicting late shipments, […]

March 2025: This post was reviewed and updated for accuracy. HAQM SageMaker Autopilot is an automated machine learning (AutoML) solution that performs all the tasks you need to complete an end-to-end machine learning (ML) workflow. It explores and prepares your data, applies different algorithms to generate a model, and transparently provides model insights and explainability […]

September 8, 2021: HAQM Elasticsearch Service has been renamed to HAQM OpenSearch Service. See details. If your contact center is serving calls over the internet, network metrics like packet loss, jitter, and round-trip time are key to understanding call quality. In the post Easily monitor call quality with HAQM Connect, we introduced a solution that […]