AWS Architecture Blog

Tag: Sustainability

Let's Architect

Let’s Architect! Designing Well-Architected systems

The design of cloud workloads can be a complex task, where a perfect and universal solution doesn’t exist. We should balance all the different trade-offs and find an optimal solution based on our context. But how does it work in practice? Which guiding principles should we follow? Which are the most important areas we should […]

Let's Architect

Let’s Architect! Architecting for sustainability

Sustainability is an important topic in the tech industry, as well as society as a whole, and defined as the ability to continue to perform a process or function over an extended period of time without depletion of natural resources or the environment. One of the key elements to designing a sustainable workload is software […]

Top-10

Top 10 AWS Architecture Blog posts of 2022

As we wrap up 2022, we want to take a moment to shine a bright light on our readers, who spend their time exploring our posts, providing generous feedback, and asking poignant questions! Much appreciation goes to our Solutions Architects, who work tirelessly to identify and produce what our customers need. Without any further ado, […]

Figure 2. Tools you can use on AWS for optimization purposes

Optimizing your AWS Infrastructure for Sustainability, Part IV: Databases

In Part I: Compute, Part II: Storage, and Part III: Networking of this series, we introduced strategies to optimize the compute, storage, and networking layers of your AWS architecture for sustainability. This post, Part IV, focuses on the database layer and proposes recommendations to optimize your databases’ utilization, performance, and queries. These recommendations are based […]

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 […]

Ask an Expert – Sustainability

In this first edition of Ask an Expert, we chat with Margaret O’Toole, Worldwide Tech Leader – Environmental Sustainability and Joseph Beer, Worldwide Tech Leader – Power and Utilities about sustainability solutions and tools to implement sustainability practices into IT design. When putting together an AWS architecture to solve business problems specifically for sustainability-focused customers, […]

A collection of posts to help you design and build sustainable cloud architecture

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. A blog can be a great starting point for you in finding and implementing a particular solution; learning about new features, services, and products; keeping up with the latest trends and ideas; […]

Shared responsibility model for sustainability

Improve workload sustainability with services and features from re:Invent 2021

At our recent annual AWS re:Invent 2021 conference, we had important announcements regarding sustainability, including the new Sustainability Pillar for AWS Well-Architected Framework and the AWS Customer Carbon Footprint Tool. In this blog post, I highlight services and features from these announcements to help you design and optimize your AWS workloads from a sustainability perspective. […]

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 […]

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 […]