Tag: machine learning

Enterprises perform quality control to ensure products meet production standards and avoid potential brand reputation damage. As the cost of sensors decreases and connectivity increases, industries adopt real-time imagery analysis to detect quality issues. At the same time, artificial intelligence (AI) advancements enable advanced automation, reduce overall cost and project time, and produce accurate defect […]

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! […]

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 […]

Running machine learning (ML) inference on large datasets is a challenge faced by many companies. There are several approaches and architecture patterns to help you tackle this problem. But no single solution may deliver the desired results for efficiency and cost effectiveness. In this blog post, we will outline a few factors that can help […]

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 […]

September’s issue of AWS Architecture Monthly issue is all about robotics. Discover why iRobot, the creator of your favorite (though maybe not your pet’s favorite) little robot vacuum, decided to move its mission-critical platform to the serverless architecture of AWS. Learn how and why you sometimes need to test in a virtual environment instead of […]

In this month’s issue of AWS Architecture Monthly, Worldwide Tech Lead for Agriculture, Karen Hildebrand (who’s also a fourth generation farmer) refers to agriculture as “the connective tissue our world needs to survive.” As our expert for August’s Agriculture issue, she also talks about what role cloud will play in future development efforts in this […]

Introduction If you’re an enterprise organization, especially in a highly regulated sector, you understand the struggle to innovate and drive change while maintaining your security and compliance posture. In particular, your banking customers’ expectations and needs are changing, and there is a broad move away from traditional branch and ATM-based services towards digital engagement. With […]

We have published a new whitepaper, Machine Learning Lens, to help you design your machine learning (ML) workloads following cloud best practices. This whitepaper gives you an overview of the iterative phases of ML and introduces you to the ML and artificial intelligence (AI) services available on AWS using scenarios and reference architectures. How often […]

In a recent customer engagement, Quantiphi, Inc., a member of the Amazon Web Services Partner Network, built a solution capable of pre-processing tens of millions of PDF documents before sending them for inference by a machine learning (ML) model. While the customer’s use case—and hence the ML model—was very specific to their needs, the pipeline that does […]