Remove Lambda Remove Scalability Remove Testing
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Streamlining Workflows with Feature Branches and Logical Stacks

Xebia

Feature branches and stack-based development approaches offer powerful ways to isolate changes, test effectively, and ensure seamless integration. When you are done, you can thoroughly test your changes before merging them into the main branch. Detecting why something failed becomes more challenging in this case.

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Enhance customer support with Amazon Bedrock Agents by integrating enterprise data APIs

AWS Machine Learning - AI

The Lambda function runs the database query against the appropriate OpenSearch Service indexes, searching for exact matches or using fuzzy matching for partial information. The Lambda function processes the OpenSearch Service results and formats them for the Amazon Bedrock agent.

Lambda 127
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Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning - AI

Before processing the request, a Lambda authorizer function associated with the API Gateway authenticates the incoming message. After it’s authenticated, the request is forwarded to another Lambda function that contains our core application logic. The code runs in a Lambda function. Implement your business logic in this file.

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From Code to Cloud: AWS Lambda CI/CD with GitHub Actions

Perficient

Introduction: Integrating GitHub Actions for Continuous Integration and Continuous Deployment (CI/CD) in AWS Lambda deployments is a modern approach to automating the software development lifecycle. After this, open AWS Lambda and create a function using Python with the default settings. In our case, we are using ap-south-1.

Lambda 52
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Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

Careful model selection, fine-tuning, configuration, and testing might be necessary to balance the impact of latency and cost with the desired classification accuracy. This hybrid approach combines the scalability and flexibility of semantic search with the precision and context-awareness of classifier LLMs.

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Track, allocate, and manage your generative AI cost and usage with Amazon Bedrock

AWS Machine Learning - AI

Without a scalable approach to controlling costs, organizations risk unbudgeted usage and cost overruns. This scalable, programmatic approach eliminates inefficient manual processes, reduces the risk of excess spending, and ensures that critical applications receive priority. However, there are considerations to keep in mind.

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

AWS Machine Learning - AI

It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. The generative AI playground is a UI provided to tenants where they can run their one-time experiments, chat with several FMs, and manually test capabilities such as guardrails or model evaluation for exploration purposes.