Amazon Bedrock has opened the door for customers to create new and delightful experiences for their customers using generative artificial intelligence (AI). This fully managed service offers a range of high-performing foundation models (FMs) from top AI companies through a single API, along with necessary capabilities to build generative AI applications with security, privacy, and responsible AI. With access to top FMs, customers are able to experiment and innovate at an accelerated pace. As customers aim to operationalize these applications, they require prescriptive ways to monitor the health and performance of these models.
To provide customers with quick and easy visibility into Amazon Bedrock workloads, an automatic dashboard feature has been introduced in CloudWatch. This dashboard allows users to gain insights into key metrics for Amazon Bedrock models, such as latency and invocation metrics. Monitoring latency performance is crucial for customer-facing applications like conversational assistants. By using the automatic dashboard, customers can monitor the health and performance of their models effectively.
In addition to the automatic dashboard, customers also have the option to build custom dashboards in CloudWatch to track metrics from multiple AWS services, aiding in debugging, performance monitoring, and custom logic implementation. A popular choice for customization is implementing Retrieval Augmented Generation (RAG) to augment models with domain-specific data. By creating a custom dashboard, users can gain insights into various components, such as Lambda functions, context retrieval latency, and OpenSearch Serverless collection, essential for monitoring RAG workflows.
A vital aspect of monitoring is attributing usage to specific applications or users. This can be achieved by utilizing Amazon Bedrock invocation logs to gain visibility into token consumption and track usage. CloudWatch Logs Insights can provide detailed breakdowns of usage by identity across invocations, allowing users to monitor and address potential issues effectively.
The authors of this post are a group of experienced professionals from Amazon CloudWatch and AWS, each specializing in different aspects of cloud observability, AIOps, machine learning, and technology. They offer valuable insights and guidance on monitoring and operationalizing generative AI applications using Amazon Bedrock and CloudWatch.
Overall, customers can leverage automatic dashboards, custom dashboard creation, and detailed usage attribution to effectively monitor and optimize their generative AI applications. By utilizing these tools and resources provided by Amazon Bedrock and CloudWatch, customers can enhance the performance, health, and usability of their applications while ensuring a seamless experience for their users.
Article Source
https://aws.amazon.com/blogs/machine-learning/improve-visibility-into-amazon-bedrock-usage-and-performance-with-amazon-cloudwatch/
