Category: Artificial Intelligence

With the number of cameras and sensors deployed growing exponentially, companies across industries are consuming more video than ever before. Additionally, advancements in analytics have expanded potential use cases, and these devices are now used to improve business operations and intelligence. In turn, the ability to effectively process video at these rapidly expanding volumes is […]

The celebrity recognition feature in Amazon Rekognition automatically recognizes tens of thousands of well-known personalities in images and videos using machine learning (ML). Celebrity recognition significantly reduces the repetitive manual effort required to tag produced media content and make it readily searchable. Starting today, we’re updating our models to provide higher accuracy (lower false detections […]

With Amazon SageMaker Pipelines, you can create, automate, and manage end-to-end machine learning (ML) workflows at scale. SageMaker Projects build on SageMaker Pipelines by providing several MLOps templates that automate model building and deployment pipelines using continuous integration and continuous delivery (CI/CD). To help you get started, SageMaker Pipelines provides many predefined step types, such […]

Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning. It provides a single, web-based visual interface where you can perform all ML development steps required to build, train, and deploy models. You can quickly upload data, create new notebooks, train and tune models, move back and forth between steps to […]

Multiple pieces of information are often required to complete a task or to process a query. For example, when talking to an insurance agent, a caller might ask, “Can you provide me quotes for home, auto, and boat?” The agent recognizes this as a list of policy types before continuing with the conversation. Automation of […]

Amazon SageMaker notebook instances now support Amazon Linux 2, so you can now create a new Amazon SageMaker notebook instance to start developing your machine learning (ML) models with the latest updates. An obvious question is: what do I need to do to migrate my work from an existing notebook instance that runs on Amazon […]

February 8th, 2022: Updated with AWS CloudFormation support to create an Amazon Linux 2 based SageMaker notebook instance. Today, we’re excited to announce that Amazon SageMaker notebook instances support Amazon Linux 2. You can now choose Amazon Linux 2 for your new SageMaker notebook instance to take advantage of the latest update and support provided […]

In Part 1 of this series of posts, we offered step-by-step guidance for using Amazon SageMaker, SageMaker projects and Amazon SageMaker Pipelines, and AWS services such as Amazon Virtual Private Cloud (Amazon VPC), AWS CloudFormation, AWS Key Management Service (AWS KMS), and AWS Identity and Access Management (IAM) to implement secure architectures for multi-account enterprise […]

Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. Although Studio provides all the tools you need to take your models from experimentation to production, you need a robust and secure model deployment process. This process must fulfill […]

Amazon Personalize now enables you to optimize personalized recommendations for a business metric of your choice, in addition to improving relevance of recommendations for your users. You can define a business metric such as revenue, profit margin, video watch time, or any other numerical attribute of your item catalog to optimize your recommendations. Amazon Personalize […]