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

AWS Machine Learning

Here are some features which we will cover: AWS CloudFormation support Private network policies for Amazon OpenSearch Serverless Multiple S3 buckets as data sources Service Quotas support Hybrid search, metadata filters, custom prompts for the RetreiveAndGenerate API, and maximum number of retrievals.

APIs 98
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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 97
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Harnessing the power of enterprise data with generative AI: Insights from Amazon Kendra, LangChain, and large language models

AWS Machine Learning

Harnessing the generative capabilities of foundational models, this tool creates convincing and compliant pharmaceutical advertisements, ensuring content adheres to industry standards and regulations. Prerequisites You must have the following prerequisites: An AWS account. The AWS Command Line Interface (AWS CLI) v2. Python 3.6

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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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Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

AWS Machine Learning

The goal of this post is to empower AI and machine learning (ML) engineers, data scientists, solutions architects, security teams, and other stakeholders to have a common mental model and framework to apply security best practices, allowing AI/ML teams to move fast without trading off security for speed.

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What is Call Center Compliance?

NobelBiz

call centers that handle patient information on behalf of healthcare providers and organizations are obligated to comply with the Health Insurance Portability and Accountability Act (HIPAA) that sets standards for the security and privacy of sensitive patient health information. HIPAA Regulations: In the U.S.,

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Philips accelerates development of AI-enabled healthcare solutions with an MLOps platform built on Amazon SageMaker

AWS Machine Learning

Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. This enables Philips ML engineers and developers to provide updates, bug fixes, and future enhancements without disrupting the entire system.