Category: Artificial Intelligence

Observability describes how well you can understand what is happening in a system, often by instrumenting it to collect metrics, logs, and traces. To achieve operational excellence and meet business objectives, you need to understand how your systems are performing. In order to accomplish this, many customers use Amazon CloudWatch to get real-time monitoring, alerts […]

To support business and internal processes, organizations are increasing their use of conversational interfaces. They offer opportunities for more availability, improved service levels, and reduced costs. As these conversational services become more important, so, does the need to monitor performance and effectiveness of these interfaces with analytics and dashboards. This analysis is used to drive […]

Amazon Bedrock is an easy way to build and scale generative AI applications with foundation models (FMs). As a fully managed service, it offers a choice of high-performing FMs from leading AI companies including AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon. It also offers a broad set of capabilities needed to build generative […]

AWS CloudTrail tracks user and API activities across AWS environments for governance and auditing purposes and allows customers to centralize a record of these activities. Customers have the option to send AWS CloudTrail logs to Amazon CloudWatch that simplifies and streamlines the analysis and monitoring of AWS CloudTrail recorded activities. Amazon CloudWatch anomaly detection allows […]

Incident response is a core operations capability for organizations to develop, and a core element in the AWS Cloud Adoption Framework (AWS CAF). Responding to operations incidents quickly is important to minimize their impacts. Automating incident response helps you scale your capabilities, rapidly reduce the recovery time, and reduce repetitive work by your cloud operations teams. […]

Artificial intelligence operations (AIOps) is the process of using machine learning techniques to solve operational problems. The goal of AIOps is to reduce human intervention in IT operations processes. By using advanced machine learning techniques, you can reduce operational incidents and increase service quality, and AIOps can help you predict incidents before they happen. Amazon […]

In this post, we’ll show how enterprises can use AWS Service Catalog to create AWS Service Catalog products based on AWS machine learning (ML) services, such as Amazon Polly and Amazon Rekognition. These products are packaged in AWS Service Catalog portfolios that customers can use for their use cases. These portfolios can generate revenue for […]

AWS allows customers to assign metadata to their AWS resources in the form of tags. Each tag consists of a customer-defined key and an optional value. Tags can make it easier to manage, search for, and filter resources by purpose, owner, environment, or other criteria. AWS tags can be used for many purposes like organizing […]

When customers begin their machine learning (ML) journey, it’s common for individual teams in a line of business (LoB) to set up their own ML environments. This provides teams with flexibility in their tooling choices, so they can move fast to meet business objectives. However, a key difference between ML projects and other IT projects is […]

Customers are constantly looking to innovate in order to remain competitive in their respective markets. One way to achieving such competitiveness is through the ability to build services and applications fast and cost effectively, thereby reducing time to market while driving down costs. One of the feedback we regularly get from customers is that, applications […]