Category: Artificial Intelligence

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

Federated Learning (FL) is an emerging approach to machine learning (ML) where model training data is not stored in a central location. During ML training, we typically need to access the entire training dataset on a single machine. For purposes of performance scaling, we divide the training data between multiple CPUs, multiple GPUs, or a […]