AWS Architecture Blog
Category: Artificial Intelligence
Architecting near real-time personalized recommendations with Amazon Personalize
Delivering personalized customer experiences enables organizations to improve business outcomes such as acquiring and retaining customers, increasing engagement, driving efficiencies, and improving discoverability. Developing an in-house personalization solution can take a lot of time, which increases the time it takes for your business to launch new features and user experiences. In this post, we show […]
Amazon Personalize customer outreach on your ecommerce platform
In the past, brick-and-mortar retailers leveraged native marketing and advertisement channels to engage with consumers. They have promoted their products and services through TV commercials, and magazine and newspaper ads. Many of them have started using social media and digital advertisements. Although marketing approaches are beginning to modernize and expand to digital channels, businesses still […]
Let’s Architect! Architecting with custom chips and accelerators
It’s hard to imagine a world without computer chips. They are at the heart of the devices that we use to work and play every day. Currently, Amazon Web Services (AWS) is offering customers the next generation of computer chip, with lower cost, higher performance, and a reduced carbon footprint. This edition of Let’s Architect! […]
Image background removal using Amazon SageMaker semantic segmentation
Many individuals are creating their own ecommerce and online stores in order to sell their products and services. This simplifies and speeds the process of getting products out to your selected markets. This is a critical key indicator for the success of your business. Artificial Intelligence/Machine Learning (AI/ML) and automation can offer you an improved […]
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, […]
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 […]
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 […]
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 […]
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 […]
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 […]