AWS Architecture Blog

Category: Artificial Intelligence

Basic architecture on how data drift is detected using Amazon SageMaker

Detecting data drift using Amazon SageMaker

As companies continue to embrace the cloud and digital transformation, they use historical data in order to identify trends and insights. This data is foundational to power tools, such as data analytics and machine learning (ML), in order to achieve high quality results. This is a time where major disruptions are not only lasting longer, […]

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ML lifecycle

Optimize AI/ML workloads for sustainability: Part 3, deployment and monitoring

We’re celebrating Earth Day 2022 from 4/22 through 4/29 with posts that highlight how to build, maintain, and refine your workloads for sustainability. AWS estimates that inference (the process of using a trained machine learning [ML] algorithm to make a prediction) makes up 90 percent of the cost of an ML model. Given with AWS you […]

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Solution architecture for multi-language notification system. It includes all the AWS services that are required in this solution. The flow is described as follows.

Build a multi-language notification system with Amazon Translate and Amazon Pinpoint

Organizations with global operations can struggle to notify their customers of any business-related announcements or notifications in different languages. Their customers want to receive notifications in their local language and communication preference. Organizations often rely on complicated third-party services or individuals to manually translate the notifications. This can lead to a loss of revenue due […]

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ML lifecycle

Optimize AI/ML workloads for sustainability: Part 2, model development

More complexity often means using more energy, and machine learning (ML) models are becoming bigger and more complex. And though ML hardware is getting more efficient, the energy required to train these ML models is increasing sharply. In this series, we’re following the phases of the Well-Architected machine learning lifecycle (Figure 1) to optimize your […]

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Figure 1. Automated form data extraction architecture

Automate your Data Extraction for Oil Well Data with Amazon Textract

Traditionally, many businesses archive physical formats of their business documents. These can be invoices, sales memos, purchase orders, vendor-related documents, and inventory documents. As more and more businesses are moving towards digitizing their business processes, it is becoming challenging to effectively manage these documents and perform business analytics on them. For example, in the Oil […]

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ML lifecycle

Optimize AI/ML workloads for sustainability: Part 1, identify business goals, validate ML use, and process data

Training artificial intelligence (AI) services and machine learning (ML) workloads uses a lot of energy—and they are becoming bigger and more complex. As an example, the Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models study estimates that a single training session for a language model like GPT-3 can have a carbon footprint […]

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Let's architect! logo

Let’s Architect! Architecting for Machine Learning

Though it seems like something out of a sci-fi movie, machine learning (ML) is part of our day-to-day lives. So often, in fact, that we may not always notice it. For example, social networks and mobile applications use ML to assess user patterns and interactions to deliver a more personalized experience. However, AWS services provide […]

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Figure 1. Architecture diagram of an anomaly detection solution for ecommerce traffic

Automating Anomaly Detection in Ecommerce Traffic Patterns

Many organizations with large ecommerce presences have procedures to detect major anomalies in their user traffic. Often, these processes use static alerts or manual monitoring. However, the ability to detect minor anomalies in traffic patterns near real-time can be challenging. Early detection of these minor anomalies in ecommerce traffic (such as website page visits and […]

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Architecture diagram

Enhance Your Contact Center Solution with Automated Voice Authentication and Visual IVR

Recently, the Accenture AWS Business Group (AABG) assisted a customer in developing a secure and personalized Interactive Voice Response (IVR) contact center experience that receives and processes payments and responds to customer inquiries. Our solution uses Amazon Connect at its core to help customers efficiently engage with customer service agents. To ensure transactions are completed […]

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Figure 2. Credit application – technical solution using Amazon SageMaker and Experian CaaS ML models

How Experian uses Amazon SageMaker to Deliver Affordability Verification 

Financial Service (FS) providers must identify patterns and signals in a customer’s financial behavior to provide deeper, up-to-the-minute, insight into their affordability and credit risk. FS providers use these insights to improve decision making and customer management capabilities. Machine learning (ML) models and algorithms play a significant role in automating, categorising, and deriving insights from […]

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