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Integrate dynamic web content in your generative AI application using a web search API and Amazon Bedrock Agents

AWS Machine Learning

Amazon Bedrock agents use LLMs to break down tasks, interact dynamically with users, run actions through API calls, and augment knowledge using Amazon Bedrock Knowledge Bases. In this post, we demonstrate how to use Amazon Bedrock Agents with a web search API to integrate dynamic web content in your generative AI application.

APIs 105
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Generate customized, compliant application IaC scripts for AWS Landing Zone using Amazon Bedrock

AWS Machine Learning

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon with a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

Scripts 127
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How Druva used Amazon Bedrock to address foundation model complexity when building Dru, Druva’s backup AI copilot

AWS Machine Learning

Dru on the backend decodes log data, deciphers error codes, and invokes API calls to troubleshoot. This approach allowed us to break the problem down into multiple steps: Identify the API route. Generate and invoke private API calls. Having similar names and synonyms in API routes make this retrieval problem more complex.

APIs 108
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Create a multimodal chatbot tailored to your unique dataset with Amazon Bedrock FMs

AWS Machine Learning

The logic flow for generating an answer to a text-image response pair routes as follows: Steps 1 and 2 – To start, a user query and corresponding image are routed through an Amazon API Gateway connection to an AWS Lambda function, which serves as the processing and orchestrating compute for the overall process. us-east-1 or bash deploy.sh

Chatbots 124
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Bring legacy machine learning code into Amazon SageMaker using AWS Step Functions

AWS Machine Learning

The best practice for migration is to refactor these legacy codes using the Amazon SageMaker API or the SageMaker Python SDK. SageMaker runs the legacy script inside a processing container. Step Functions is a serverless workflow service that can control SageMaker APIs directly through the use of the Amazon States Language.

Scripts 136
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Introducing SageMaker Core: A new object-oriented Python SDK for Amazon SageMaker

AWS Machine Learning

In this post, we show how the SageMaker Core SDK simplifies the developer experience while providing API for seamlessly executing various steps in a general ML lifecycle. For instance, to create a training job, Boto3 offers a create_training_job API, but retrieving job details requires the describe_training_job API.

APIs 85
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How LotteON built a personalized recommendation system using Amazon SageMaker and MLOps

AWS Machine Learning

Problem definition Traditionally, the recommendation service was mainly provided by identifying the relationship between products and providing products that were highly relevant to the product selected by the customer. Lambda receives the list of recommendations and provides them to the API gateway.