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

AWS Machine Learning

Amazon Bedrock empowers teams to generate Terraform and CloudFormation scripts that are custom fitted to organizational needs while seamlessly integrating compliance and security best practices. Traditionally, cloud engineers learning IaC would manually sift through documentation and best practices to write compliant IaC scripts.

Scripts 131
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25 Call Center Leaders Share the Most Effective Ways to Boost Contact Center Efficiency

Callminer

Bill Dettering is the CEO and Founder of Zingtree , a SaaS solution for building interactive decision trees and agent scripts for contact centers (and many other industries). Interactive agent scripts from Zingtree solve this problem. Agents can also send feedback directly to script authors to further improve processes.

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Using Agents for Amazon Bedrock to interactively generate infrastructure as code

AWS Machine Learning

Agents for Amazon Bedrock automates the prompt engineering and orchestration of user-requested tasks. This solution uses Retrieval Augmented Generation (RAG) to ensure the generated scripts adhere to organizational needs and industry standards. A GitHub account with a repository to store the generated Terraform scripts.

Scripts 138
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Best practices to build generative AI applications on AWS

AWS Machine Learning

We provide an overview of key generative AI approaches, including prompt engineering, Retrieval Augmented Generation (RAG), and model customization. Building large language models (LLMs) from scratch or customizing pre-trained models requires substantial compute resources, expert data scientists, and months of engineering work.

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Building scalable, secure, and reliable RAG applications using Knowledge Bases for Amazon Bedrock

AWS Machine Learning

As you build applications on AWS, aligning RAG applications with the AWS Well-Architected Framework provides a solid foundation for building enterprise-grade solutions that drive business value while adhering to industry standards. It calls the CreateDataSource and DeleteDataSource APIs. Nitin Eusebius is a Sr.

APIs 128
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Improving your LLMs with RLHF on Amazon SageMaker

AWS Machine Learning

Reinforcement Learning from Human Feedback (RLHF) is recognized as the industry standard technique for ensuring large language models (LLMs) produce content that is truthful, harmless, and helpful. Gone are the days when you need unnatural prompt engineering to get base models, such as GPT-3, to solve your tasks. yaml ppo_hh.py

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Deploy large models at high performance using FasterTransformer on Amazon SageMaker

AWS Machine Learning

There is no industry standard for distillation, and many techniques are experimental. Prompt engineering Prompt engineering refers to efforts to extract accurate, consistent, and fair outputs from large models, such text-to-image synthesizers or large language models.