Category: Artificial Intelligence

As applications scale, customers need more automated practices to maintain application availability and reduce the time and effort spent detecting, debugging, and resolving operational issues. Organizations allocate money and developer time to deploy and manage various monitoring tools, while also dedicating considerable effort to training teams on their usage. When issues arise, operators navigate through […]

Have you ever been investigating a problem and opened up a log file and thought “I have no idea what I am looking at. If only I could get a summary of the data.” Observability and log data play an important role in maintaining operational excellence and ensuring the reliability of your applications and services. […]

With the pace of innovation with Generative AI applications, there is increasing demand for more granular observability into applications using Large Language Models (LLMs). Specifically, customers want visibility into: Prompt metrics like token usage, costs, and model IDs for individual transactions and operations, apart from service-level aggregations. Output quality factors including potential toxicity, harm, truncation […]

With the emergence of generative AI being incorporated into every aspect of how we utilize technology, a common question that customers are asking is how to properly audit generative AI services on AWS, such as Amazon Bedrock, Amazon Sagemaker, Amazon Q Developer, and Amazon Q Business. In this post, we will demonstrate common scenarios that […]

Overview When operating complex, distributed systems in the cloud, quickly identifying the root cause of issues and resolving incidents can be a daunting task. Troubleshooting often involves sifting through metrics, logs, and traces from multiple AWS services, making it challenging to gain a comprehensive understanding of the problem. So how can you streamline this process […]

Amazon Q, is a new generative artificial intelligence- (AI)-powered assistant designed for work that can be tailored to your business. You can use Amazon Q to have conversations, solve problems, generate content, gain insights, and take action by connecting to your company’s information repositories, code, data, and enterprise systems. Amazon Q provides immediate, relevant information […]

The recent rise of generative artificial intelligence (generative AI) solutions presents challenges to migrations that are in flight and to migrations that are just beginning. The business problem is that generative AI complicates cloud migrations by introducing additional risks related to data isolation, data sharing, and service costs. For example, the US Space Force has […]

AWS CloudTrail tracks user and API activities across your AWS environments for governance and auditing purposes. Large enterprises typically use multiple AWS accounts, and many of those accounts might need access to a data lake managed by a single AWS account. By using Lake Formation integration with CloudTrail Lake, you can securely aggregate the data […]

AWS Config enables businesses to assess, audit, and evaluate the configurations of their AWS resources by leveraging AWS Config rules that represent your ideal configuration settings.  For example a Security Group that allows ingress on port 22 should be marked as noncompliant. AWS Config provides predefined rules called managed rules to help you quickly get […]

Introduction The integration of Generative AI into cloud governance transforms AWS account management into a more automated and efficient process. Leveraging the generative AI capabilities of Amazon Bedrock alongside tools such as AWS Control Tower and Account Factory for Terraform (AFT), organizations can now expedite the AWS account setup and management process, aligning with best […]