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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 142
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How to Successfully Implement Customer Journey Analytics – Part 1

Pointillist

Companies are increasingly benefiting from customer journey analytics across marketing and customer experience, as the results are real, immediate and have a lasting effect. Learning how to choose the best customer journey analytics platform is just the start. Steps to Implement Customer Journey Analytics. By Swati Sahai.

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Few-shot prompt engineering and fine-tuning for LLMs in Amazon Bedrock

AWS Machine Learning

Traditionally, earnings call scripts have followed similar templates, making it a repeatable task to generate them from scratch each time. On the other hand, generative artificial intelligence (AI) models can learn these templates and produce coherent scripts when fed with quarterly financial data.

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Derive generative AI powered insights from Alation Cloud Services using Amazon Q Business Custom Connector

AWS Machine Learning

Headquartered in Redwood City, California, Alation is an AWS Specialization Partner and AWS Marketplace Seller with Data and Analytics Competency. Organizations trust Alations platform for self-service analytics, cloud transformation, data governance, and AI-ready data, fostering innovation at scale. secrets_manager_client = boto3.client('secretsmanager')

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Generate training data and cost-effectively train categorical models with Amazon Bedrock

AWS Machine Learning

Refer to Getting started with the API to set up your environment to make Amazon Bedrock requests through the AWS API. Test the code using the native inference API for Anthropics Claude The following code uses the native inference API to send a text message to Anthropics Claude. client = boto3.client("bedrock-runtime",

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CBRE and AWS perform natural language queries of structured data using Amazon Bedrock

AWS Machine Learning

AWS Prototyping successfully delivered a scalable prototype, which solved CBRE’s business problem with a high accuracy rate (over 95%) and supported reuse of embeddings for similar NLQs, and an API gateway for integration into CBRE’s dashboards. The following diagram illustrates the web interface and API management layer.

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Derive meaningful and actionable operational insights from AWS Using Amazon Q Business

AWS Machine Learning

In this post, we’re using the APIs for AWS Support , AWS Trusted Advisor , and AWS Health to programmatically access the support datasets and use the Amazon Q Business native Amazon Simple Storage Service (Amazon S3) connector to index support data and provide a prebuilt chatbot web experience. Synchronize the data source to index the data.

Scripts 121