AWS Architecture Blog

Category: Amazon Machine Learning

Figure 1. Well-Architected Machine Learning Lifecycle

Introducing the new AWS Well-Architected Machine Learning Lens

The AWS Well-Architected Framework provides you with a formal approach to compare your workloads against best practices. It also includes guidance on how to make improvements. Machine learning (ML) algorithms discover and learn patterns in data, and construct mathematical models to predict future data. These solutions can revolutionize lives through better diagnoses of diseases, environmental […]

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Field Notes: Build a Cross-Validation Machine Learning Model Pipeline at Scale with Amazon SageMaker

When building a machine learning algorithm, such as a regression or classification algorithm, a common goal is to produce a generalized model. This is so that it performs well on new data that the model has not seen before. Overfitting and underfitting are two fundamental causes of poor performance for machine learning models. A model […]

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Training workflow

Classifying Millions of Amazon items with Machine Learning, Part I: Event Driven Architecture

As part of AWS Professional Services, we work with customers across different industries to understand their needs and supplement their teams with specialized skills and experience. Some of our customers are internal teams from the Amazon retail organization who request our help with their initiatives. One of these teams, the Global Environmental Affairs team, identifies […]

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Figure 1. Notional architecture for improving forecasting accuracy solution and SAP integration

Improving Retail Forecast Accuracy with Machine Learning

The global retail market continues to grow larger and the influx of consumer data increases daily. The rise in volume, variety, and velocity of data poses challenges with demand forecasting and inventory planning. Outdated systems generate inaccurate demand forecasts. This results in multiple challenges for retailers. They are faced with over-stocking and lost sales, and […]

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How to redact confidential information in your ML pipeline

Integrating Redaction of FinServ Data into a Machine Learning Pipeline

Financial companies process hundreds of thousands of documents every day. These include loan and mortgage statements that contain large amounts of confidential customer information. Data privacy requires that sensitive data be redacted to protect the customer and the institution. Redacting digital and physical documents is time-consuming and labor-intensive. The accidental or inadvertent release of personal information […]

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Figure 1 - Architecture for Automating Data Ingestion and Labeling for Autonomous Vehicle Development

Field Notes: Automating Data Ingestion and Labeling for Autonomous Vehicle Development

This post was co-written by Amr Ragab, AWS Sr. Solutions Architect, EC2 Engineering and Anant Nawalgaria, former AWS Professional Services EMEA. One of the most common needs we have heard from customers in Autonomous Vehicle (AV) development, is to launch a hybrid deployment environment at scale. As vehicle fleets are deployed across the globe, they […]

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Figure 1. Enterprise customer engagement channels and corresponding AWS services

Architecting Cross-channel Intelligent Customer Engagements

Recently, we have had customers express the desire to build “omni-channels.” These omni-channels provide a centralized overview of digital engagement channels that help you better understand your customers and offer a more personalized experience. Many companies have tried or are trying to implement an omni-channel strategy. However, because most existing channels are built on different platforms and […]

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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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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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