AWS Cloud Operations Blog
Category: Artificial Intelligence
Using AWS CloudTrail to propagate tags across related AWS resources – Part 2
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 […]
Setting up secure, well-governed machine learning environments on AWS
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 […]
Building and deploying a serverless app using AWS Serverless Application Model and AWS CloudFormation
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 […]
How managed service providers can use AWS Control Tower to provide services
AWS Control Tower is a managed AWS service that automates the creation of a multi-account AWS environment based upon the AWS Well-Architected Framework. It builds the environment using AWS best practices for security and management services. In this blog post, we’ll show how a managed service provider can use AWS Control Tower and AWS Service […]
Building secure Amazon SageMaker access URLs with AWS Service Catalog
Many customers need a secure method to access Amazon SageMaker notebooks within their private network without logging in to the AWS console, or using the AWS CLI/SDKs. This may be desired for enhanced security or to provide an easier self-service path for data scientists. In this blog post, we show you a how to connect […]
Deliver ML-powered operational insights to your on-call teams via PagerDuty with Amazon DevOps Guru
Amazon DevOps Guru, now in preview, is an ML-powered cloud operations service that assists you in improving application availability. It’s easy to set up and use, and leverages machine learning models informed by years of operational expertise in building, scaling, and maintaining highly available applications at Amazon.com. DevOps Guru continuously analyzes streams of disparate data […]
Analyzing Amazon Lex conversation log data with Amazon CloudWatch Insights
Conversational interfaces like chatbots have become an important channel for brands to communicate with their customers, partners, and employees. They help with faster service, 24/7 availability, and reduced service costs. By monitoring conversations between your customers and the bot, you can gain insights into user interactions, trends, and missed utterances. The additional insights will help […]
Enable self-service, secured data science using Amazon SageMaker notebooks and AWS Service Catalog
by Sanjay Garje and Vebhhav (Veb) Singh Enterprises of all sizes are moving to the AWS Cloud. We hear from leadership of those enterprise teams that they are looking to provide a safe, cost-governed way to provide easy access to Amazon SageMaker to promote experimentation with data science to unlock new business opportunities and disrupt […]