AWS for Industries

Tag: machine learning

In the News: Amazon Web Services’ Unique Value Proposition in Scaling Autonomous Vehicle (AV) Development and Deployment

This article originally appeared on Frost & Sullivan Within AVs, today’s focus is on improving the software stack’s detection, classification, path planning, and motion control modules while also securing the potential cyber vulnerabilities that may arise due to the increased electronic content. Connected and automated vehicles (CAVs) signal the start of a transformative era in the […]

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Practical Applications of Artificial Intelligence and Machine Learning to Fuel Growth for CPG Companies

The concept of artificial intelligence (AI) has been around for centuries as philosophers and thinkers pondered the possibility of creating machines capable of thinking and functioning like humans. The advent of computers in the early 20th century finally placed the promise of AI within reach. However, computer scientists realized they needed enormous amounts of data […]

Recap of re:Invent 2021 for the CPG Industry

A couple of weeks ago at AWS re:Invent 2021, we announced more than 50 new products and services. If you have time, be sure to watch Adam Selipsky’s keynote address to hear major news, and also read the Top Announcement of AWS re:Invent 2021 blog post to learn even more details about some of our […]

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What’s New in Retail from re:Invent 2021

It’s been two years since AWS hosted an in person re:Invent conference. Although everyone had masks on, the excitement was palpable. I had a great time meeting with customers in a face-to-face setting again with dinners and parties. It was really fun. And with so much news coming out of the event, I’m sure it […]

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In the News: Amazon Web Services’ Edge to Cloud Approach to Drive a New Era of Software-Defined Vehicles

This article originally appeared on Frost & Sullivan The pandemic has changed how mobility will be perceived over this decade and beyond. Industry stakeholders are course-correcting; channeling investments away from traditional hardware-focused approaches into a software-centric future, enabling new on-demand services and new revenue streams. This will lay the foundation for a new generation of […]

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Using Amazon Athena and Amazon SageMaker for Improved Customer Churn Analysis

Customer churn is a growing concern for many companies—and not without merit. During the pandemic, 75% of US consumers switched brands or stores, and 60% of them plan to incorporate new buying patterns and habits post-COVID. In other words, 45% of consumers shifted their brand choices over the long term. But customer churn is hardly […]

Deriving AI/ML-driven insights from healthcare data using Amazon HealthLake

Derive AI/ML-driven insights from healthcare data using Amazon HealthLake

Last year, we announced Amazon HealthLake, a new HIPAA-eligible service for healthcare providers, health insurance companies, and pharmaceutical companies to securely store, transform, query, analyze, and share health data in the cloud, at petabyte scale. We launched HealthLake to address one of the key challenges that the health industry – from healthcare payers to life […]

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AI-Driven Visual Inspection of Wind Turbines Based on Drone Imaging

Introduction Keeping wind turbines operational also means keeping them well maintained. One of the steps in maintenance is regular visual inspection. Wind farm operation and maintenance companies started to utilize drones with attached cameras to perform visual inspections. According to a recent study, drone-based inspection reduces costs by up to 70% and decreases revenue lost […]

Training Machine Learning Models on Multimodal Health Data with Amazon SageMaker

Training Machine Learning Models on Multimodal Health Data with Amazon SageMaker

This post was co-authored by Olivia Choudhury, PhD, Partner Solutions Architect; Michael Hsieh, Sr. AI/ML Specialist Solutions Architect; and Andy Schuetz, PhD, Sr. Startup Solutions Architect at AWS. This is the second blog post in a two-part series on Multimodal Machine Learning (Multimodal ML). In part one, we deployed pipelines for processing RNA sequence data, clinical […]

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Building Scalable Machine Learning Pipelines for Multimodal Health Data on AWS

This post was co-authored by Olivia Choudhury, PhD, Partner Solutions Architect; Michael Hsieh, Senior AI/ML Specialist Solutions Architect; and Andy Schuetz, PhD, Sr. Partner Solutions Architect. Healthcare and life sciences organizations use machine learning (ML) to enable precision medicine, anticipate patient preferences, detect disease, improve care quality, and understand inequities. Rapid growth in health information […]