Category: Management & Governance

In this post, we walk you through the process to build an automated mechanism using HAQM SageMaker to process your log data, run training iterations over it to obtain the best-performing anomaly detection model, and register it with the HAQM SageMaker Model Registry for your customers to use it.

This post serves as a step-by-step guide on how to set up lifecycle configurations for your HAQM SageMaker Studio domains. With lifecycle configurations, system administrators can apply automated controls to their SageMaker Studio domains and their users. We cover core concepts of SageMaker Studio and provide code examples of how to apply lifecycle configuration to […]

This post dives deep into how to set up data governance at scale using HAQM DataZone for the data mesh. The data mesh is a modern approach to data management that decentralizes data ownership and treats data as a product. It enables different business units within an organization to create, share, and govern their own data assets, promoting self-service analytics and reducing the time required to convert data experiments into production-ready applications.

This blog post discusses a solution that combines AWS Resilience Hub and HAQM Bedrock to generate architectural findings in natural language. By using the capabilities of Resilience Hub and HAQM Bedrock, you can share findings with C-suite executives, engineers, managers, and other personas within your corporation to provide better visibility over maintaining a resilient architecture.

In this post, we share how Hearst, one of the nation’s largest global, diversified information, services, and media companies, overcame these challenges by creating a self-service generative AI conversational assistant for business units seeking guidance from their CCoE.

In this post, we show you how to create an AI-powered, event-driven operations assistant that automatically responds to operational events. The assistant can filter out irrelevant events (based on your organization’s policies), recommend actions, create and manage issue tickets in integrated IT service management (ITSM) tools to track actions, and query knowledge bases for insights related to operational events.

In this post, we discuss scaling up generative AI for different lines of businesses (LOBs) and address the challenges that come around legal, compliance, operational complexities, data privacy and security.

In this post, we analyze strategies for governing access to HAQM Bedrock and SageMaker JumpStart models from within SageMaker Canvas using AWS Identity and Access Management (IAM) policies. You’ll learn how to create granular permissions to control the invocation of ready-to-use HAQM Bedrock models and prevent the provisioning of SageMaker endpoints with specified SageMaker JumpStart models.

As generative artificial intelligence (AI) applications become more prevalent, maintaining responsible AI principles becomes essential. Without proper safeguards, large language models (LLMs) can potentially generate harmful, biased, or inappropriate content, posing risks to individuals and organizations. Applying guardrails helps mitigate these risks by enforcing policies and guidelines that align with ethical principles and legal requirements.HAQM […]

As you navigate the complexities of cloud migration, the need for a structured, secure, and compliant environment is paramount. AWS Landing Zone addresses this need by offering a standardized approach to deploying AWS resources. This makes sure your cloud foundation is built according to AWS best practices from the start. With AWS Landing Zone, you eliminate the guesswork in security configurations, resource provisioning, and account management. It’s particularly beneficial for organizations looking to scale without compromising on governance or control, providing a clear path to a robust and efficient cloud setup. In this post, we show you how to generate customized, compliant IaC scripts for AWS Landing Zone using HAQM Bedrock.