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GraphStorm 0.3: Scalable, multi-task learning on graphs with user-friendly APIs

AWS Machine Learning

adds new APIs to customize GraphStorm pipelines: you now only need 12 lines of code to implement a custom node classification training loop. To help you get started with the new API, we have published two Jupyter notebook examples: one for node classification, and one for a link prediction task. Specifically, GraphStorm 0.3

APIs 107
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Best practices for building robust generative AI applications with Amazon Bedrock Agents – Part 2

AWS Machine Learning

In Part 1 of this series, we explored best practices for creating accurate and reliable agents using Amazon Bedrock Agents. The agent can use company APIs and external knowledge through Retrieval Augmented Generation (RAG). We also recommend that you get started using our Agent Blueprints construct.

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Achieve operational excellence with well-architected generative AI solutions using Amazon Bedrock

AWS Machine Learning

Because this is an emerging area, best practices, practical guidance, and design patterns are difficult to find in an easily consumable basis. This integration makes sure enterprises can take advantage of the full power of generative AI while adhering to best practices in operational excellence.

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Create a generative AI-based application builder assistant using Amazon Bedrock Agents

AWS Machine Learning

These agentic workflows decompose the natural language query-based tasks into multiple actionable steps with iterative feedback loops and self-reflection to produce the final result using tools and APIs. What are some S3 best practices? What is the total revenue for each employee?

APIs 88
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How Aviva built a scalable, secure, and reliable MLOps platform using Amazon SageMaker

AWS Machine Learning

In this post, we describe how Aviva built a fully serverless MLOps platform based on the AWS Enterprise MLOps Framework and Amazon SageMaker to integrate DevOps best practices into the ML lifecycle. We illustrate the entire setup of the MLOps platform using a real-world use case that Aviva has adopted as its first ML use case.

APIs 82
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Improve the performance of your Generative AI applications with Prompt Optimization on Amazon Bedrock

AWS Machine Learning

Prompt engineering refers to the practice of writing instructions to get the desired responses from foundation models (FMs). You might have to spend months experimenting and iterating on your prompts, following the best practices for each model, to achieve your desired output. Question: What is the Eiffel Tower?

Benchmark 117
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Build an Amazon SageMaker Model Registry approval and promotion workflow with human intervention

AWS Machine Learning

The solution uses AWS Lambda , Amazon API Gateway , Amazon EventBridge , and SageMaker to automate the workflow with human approval intervention in the middle. The EventBridge model registration event rule invokes a Lambda function that constructs an email with a link to approve or reject the registered model.

APIs 115