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Build generative AI applications quickly with Amazon Bedrock IDE in Amazon SageMaker Unified Studio

AWS Machine Learning

For example, by the end of this tutorial, you will be able to query the data with prompts such as “Can you return our five top selling products this quarter and the principal customer complaints for each?” This will provision the backend infrastructure and services that the sales analytics application will rely on.

APIs 106
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Build a video insights and summarization engine using generative AI with Amazon Bedrock

AWS Machine Learning

These insights are stored in a central repository, unlocking the ability for analytics teams to have a single view of interactions and use the data to formulate better sales and support strategies. With Lambda integration, we can create a web API with an endpoint to the Lambda function.

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Integrate generative AI capabilities into Microsoft Office using Amazon Bedrock

AWS Machine Learning

One can quickly host such application on the AWS Cloud without managing the underlying infrastructure, for example, with Amazon Simple Storage Service (S3) and Amazon CloudFront. Note that these APIs use objects as namespaces, alleviating the need for explicit imports. Here, we use Anthropics Claude 3.5 Sonnet).

APIs 111
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AI Workforce: using AI and Drones to simplify infrastructure inspections

AWS Machine Learning

As an example, climbing a wind turbine in bad weather for an inspection can be dangerous. The following figure shows an example of the user dashboard and drone conversation. The following figure is an example of drone 4K footage. Plus, even the best human inspector can miss things.

APIs 113
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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning

It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. It contains services used to onboard, manage, and operate the environment, for example, to onboard and off-board tenants, users, and models, assign quotas to different tenants, and authentication and authorization microservices.

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Discover insights from Gmail using the Gmail connector for Amazon Q Business

AWS Machine Learning

Then we provide examples of how to use the AI-powered chat interface to gain insights from the connected data source. We provide the service account with authorization scopes to allow access to the required Gmail APIs. In our example, we name the project GmailConnector. Choose Enable to enable this API. Choose Create.

APIs 119
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Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval

AWS Machine Learning

SageMaker is a data, analytics, and AI/ML platform, which we will use in conjunction with FMEval to streamline the evaluation process. It functions as a standalone HTTP server that provides various REST API endpoints for monitoring, recording, and visualizing experiment runs. We specifically focus on SageMaker with MLflow.