AWS Machine Learning Blog

Category: Technical How-to

Elevate your marketing solutions with Amazon Personalize and generative AI

Generative artificial intelligence is transforming how enterprises do business. Organizations are using AI to improve data-driven decisions, enhance omnichannel experiences, and drive next-generation product development. Enterprises are using generative AI specifically to power their marketing efforts through emails, push notifications, and other outbound communication channels. Gartner predicts that “by 2025, 30% of outbound marketing messages […]

Figure 1 – Transmittance characteristics of methane in the SWIR spectrum and coverage of Sentinel-2 multi-spectral missions

Detection and high-frequency monitoring of methane emission point sources using Amazon SageMaker geospatial capabilities

Methane (CH4) is a major anthropogenic greenhouse gas that‘s a by-product of oil and gas extraction, coal mining, large-scale animal farming, and waste disposal, among other sources. The global warming potential of CH4 is 86 times that of CO2 and the Intergovernmental Panel on Climate Change (IPCC) estimates that methane is responsible for 30 percent of observed […]

Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have further sped up the need of ML adoption across industries. However, implementing security, data privacy, and governance controls are still key challenges faced by customers when implementing ML […]

Defect detection in high-resolution imagery using two-stage Amazon Rekognition Custom Labels models

High-resolution imagery is very prevalent in today’s world, from satellite imagery to drones and DLSR cameras. From this imagery, we can capture damage due to natural disasters, anomalies in manufacturing equipment, or very small defects such as defects on printed circuit boards (PCBs) or semiconductors. Building anomaly detection models using high-resolution imagery can be challenging […]

Automatically redact PII for machine learning using Amazon SageMaker Data Wrangler

Customers increasingly want to use deep learning approaches such as large language models (LLMs) to automate the extraction of data and insights. For many industries, data that is useful for machine learning (ML) may contain personally identifiable information (PII). To ensure customer privacy and maintain regulatory compliance while training, fine-tuning, and using deep learning models, […]

How Veriff decreased deployment time by 80% using Amazon SageMaker multi-model endpoints

Veriff is an identity verification platform partner for innovative growth-driven organizations, including pioneers in financial services, FinTech, crypto, gaming, mobility, and online marketplaces. In this post, we show you how Veriff standardized their model deployment workflow using Amazon SageMaker, reducing costs and development time.

Improve performance of Falcon models with Amazon SageMaker

What is the optimal framework and configuration for hosting large language models (LLMs) for text-generating generative AI applications? Despite the abundance of options for serving LLMs, this is a hard question to answer due to the size of the models, varying model architectures, performance requirements of applications, and more. The Amazon SageMaker Large Model Inference […]

Index your web crawled content using the new Web Crawler for Amazon Kendra

In this post, we show how to index information stored in websites and use the intelligent search in Amazon 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.

FL-architecture

Reinventing a cloud-native federated learning architecture on AWS

In this blog, you will learn to build a cloud-native FL architecture on AWS. By using infrastructure as code (IaC) tools on AWS, you can deploy FL architectures with ease. Also, a cloud-native architecture takes full advantage of a variety of AWS services with proven security and operational excellence, thereby simplifying the development of FL.

Use no-code machine learning to derive insights from product reviews using Amazon SageMaker Canvas sentiment analysis and text analysis models

According to Gartner, 85% of software buyers trust online reviews as much as personal recommendations. Customers provide feedback and reviews about products they have purchased through many channels, including review websites, vendor websites, sales calls, social media, and many others. The problem with the increasing volume of customer reviews across multiple channels is that it […]