AWS Architecture Blog

Category: Artificial Intelligence

2020

Top 15 Architecture Blog Posts of 2020

The goal of the AWS Architecture Blog is to highlight best practices and provide architectural guidance. We publish thought leadership pieces that encourage readers to discover other technical documentation, such as solutions and managed solutions, other AWS blogs, videos, reference architectures, whitepapers, and guides, Training & Certification, case studies, and the AWS Architecture Monthly Magazine. […]

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Video Redaction - Multiprocessing

Field Notes: Speed Up Redaction of Connected Car Data by Multiprocessing Video Footage with Amazon Rekognition

In the blog, Redacting Personal Data from Connected Cars Using Amazon Rekognition, we demonstrated how you can redact personal data such as human faces using Amazon Rekognition. Traversing the video, frame by frame, and identifying personal information in each frame takes time. This solution is great for small video clips, where you do not need […]

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Field Notes: Improving Call Center Experiences with Iterative Bot Training Using Amazon Connect and Amazon Lex

This post was co-written by Abdullah Sahin, senior technology architect at Accenture, and Muhammad Qasim, software engineer at Accenture.  Organizations deploying call-center chat bots are interested in evolving their solutions continuously, in response to changing customer demands. When developing a smart chat bot, some requests can be predicted (for example following a new product launch […]

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Amazon Personalize: from datasets to a recommendation API

Automating Recommendation Engine Training with Amazon Personalize and AWS Glue

Customers from startups to enterprises observe increased revenue when personalizing customer interactions. Still, many companies are not yet leveraging the power of personalization, or, are relying solely on rule-based strategies. Those strategies are effort-intensive to maintain and not effective. Common reasons for not launching machine learning (ML) based personalization projects include: the complexity of aggregating […]

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Field Notes: Applying Machine Learning to Vegetation Management using Amazon SageMaker

This post was co-written by Louis Lim, a manager in Accenture AWS Business Group, and Soheil Moosavi, a data scientist consultant in Accenture Applied Intelligence (AAI) team. Virtually every electric customer in the US and Canada has, at one time or another, experienced a sustained electric outage as a direct result of a tree and […]

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Field Notes: Comparing Algorithm Performance Using MLOps and the AWS Cloud Development Kit

Comparing machine learning algorithm performance is fundamental for machine learning practitioners, and data scientists. The goal is to evaluate the appropriate algorithm to implement for a known business problem. Machine learning performance is often correlated to the usefulness of the model deployed. Improving the performance of the model typically results in an increased accuracy of […]

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Olympus Tower - Grov Technologies

Building a Controlled Environment Agriculture Platform

This post was co-written by Michael Wirig, Software Engineering Manager at Grōv Technologies. A substantial percentage of the world’s habitable land is used for livestock farming for dairy and meat production. The dairy industry has leveraged technology to gain insights that have led to drastic improvements and are continuing to accelerate. A gallon of milk […]

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architecture for the solution

Real-Time In-Stream Inference with AWS Kinesis, SageMaker, & Apache Flink

As businesses race to digitally transform, the challenge is to cope with the amount of data, and the value of that data diminishes over time. The challenge is to analyze, learn, and infer from real-time data to predict future states, as well as to detect anomalies and get accurate results. In this blog post, we’ll […]

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SIH: Emvironment in AWS Cloud-2

Fast and Cost-Effective Image Manipulation with Serverless Image Handler

As a modern company, you most likely have both a web-based and mobile app platform to provide content to customers who view it on a range of devices. This means you need to store multiple versions of images, depending on the device. The resulting image management can be a headache as it can be expensive […]

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ML Solution Architecture

Field Notes: Gaining Insights into Labeling Jobs for Machine Learning

In an era where more and more data is generated, it becomes critical for businesses to derive value from it. With the help of supervised learning, it is possible to generate models to automatically make predictions or decisions by leveraging historical data. For example, image recognition for self-driving cars, predicting anomalies on X-rays, fraud detection […]

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