AWS Machine Learning Blog

NOVEMBER 7, 2024

In this second part, we expand the solution and show to further accelerate innovation by centralizing common Generative AI components. We also dive deeper into access patterns, governance, responsible AI, observability, and common solution designs like Retrieval Augmented Generation. This logic sits in a hybrid search component.

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AWS Machine Learning Blog

FEBRUARY 12, 2025

You can use metadata filtering to narrow down search results by specifying inclusion and exclusion criteria. For a demonstration on how you can use a RAG evaluation framework in Amazon Bedrock to compute RAG quality metrics, refer to New RAG evaluation and LLM-as-a-judge capabilities in Amazon Bedrock.

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IBM Journey to AI blog

OCTOBER 16, 2023

AI governance refers to the practice of directing, managing and monitoring an organization’s AI activities. It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. Generative AI chatbots have been known to insult customers and make up facts.

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AWS Machine Learning Blog

OCTOBER 31, 2024

This request contains the user’s message and relevant metadata. The Lambda function interacts with Amazon Bedrock through its runtime APIs, using either the RetrieveAndGenerate API that connects to a knowledge base, or the Converse API to chat directly with an LLM available on Amazon Bedrock.

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AWS Machine Learning Blog

AUGUST 9, 2024

RAG enables LLMs to generate more relevant, accurate, and contextual responses by cross-referencing an organization’s internal knowledge base or specific domains, without the need to retrain the model. The embedding representations of text chunks along with related metadata are indexed in OpenSearch Service.

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AWS Machine Learning Blog

AUGUST 8, 2024

Twilio’s use case Twilio wanted to provide an AI assistant to help their data analysts find data in their data lake. They used the metadata layer (schema information) over their data lake consisting of views (tables) and models (relationships) from their data reporting tool, Looker , as the source of truth.

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NVIDIA

MAY 1, 2024

Say It Out Loud ChatRTX uses retrieval-augmented generation , NVIDIA TensorRT-LLM software and NVIDIA RTX acceleration to bring chatbot capabilities to RTX-powered Windows PCs and workstations. The latest version adds support for additional LLMs, including Gemma, the latest open, local LLM trained by Google.

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Marktechpost

MAY 30, 2024

Participants learn to build metadata for documents containing text and images, retrieve relevant text chunks, and print citations using Multimodal RAG with Gemini. Introduction to Responsible AI This course explains what responsible AI is, its importance, and how Google implements it in its products.

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AWS Machine Learning Blog

MARCH 5, 2025

To scale ground truth generation and curation, you can apply a risk-based approach in conjunction with a prompt-based strategy using LLMs. Its important to note that LLM-generated ground truth isnt a substitute for use case SME involvement. To convert the source document excerpt into ground truth, we provide a base LLM prompt template.

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AWS Machine Learning Blog

SEPTEMBER 13, 2024

The image damage analysis notification agent is responsible for doing a preliminary analysis of the images uploaded for a damage. This agent invokes a Lambda function that internally calls the Anthropic Claude Sonnet large language model (LLM) on Amazon Bedrock to perform preliminary analysis on the images.

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AWS Machine Learning Blog

MAY 7, 2024

Building enhanced semantic search capabilities that analyze media contextually would lay the groundwork for creating AI-generated content, allowing customers to produce customized media more efficiently. With recent advances in large language models (LLMs), Veritone has updated its platform with these powerful new AI capabilities.

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Flipboard

AUGUST 6, 2023

The tool connects Jupyter with large language models (LLMs) from various providers, including AI21, Anthropic, AWS, Cohere, and OpenAI, supported by LangChain. Designed with responsible AI and data privacy in mind, Jupyter AI empowers users to choose their preferred LLM, embedding model, and vector database to suit their specific needs.

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Towards AI

AUGUST 8, 2024

Take advantage of the current deal offered by Amazon (depending on location) to get our recent book, “Building LLMs for Production,” with 30% off right now! Featured Community post from the Discord Arwmoffat just released Manifest, a tool that lets you write a Python function and have an LLM execute it.

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Mlearning.ai

JULY 8, 2023

Introduction Create ML Ops for LLM’s Build end to end development and deployment cycle. Add Responsible AI to LLM’s Add Abuse detection to LLM’s. High level process and flow LLM Ops is people, process and technology. LLM Ops flow — Architecture Architecture explained.

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ODSC - Open Data Science

AUGUST 24, 2023

Evolving Trends in Prompt Engineering for Large Language Models (LLMs) with Built-in Responsible AI Practices Editor’s note: Jayachandran Ramachandran and Rohit Sroch are speakers for ODSC APAC this August 22–23. are harnessed to channel LLMs output. Auto Eval Common Metric Eval Human Eval Custom Model Eval 3.

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AWS Machine Learning Blog

MAY 21, 2024

This blog post outlines various use cases where we’re using generative AI to address digital publishing challenges. However, when the article is complete, supporting information and metadata must be defined, such as an article summary, categories, tags, and related articles.

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AWS Machine Learning Blog

SEPTEMBER 28, 2023

This means companies need loose coupling between app clients (model consumers) and model inference endpoints, which ensures easy switch among large language model (LLM), vision, or multi-modal endpoints if needed. This table will hold the endpoint, metadata, and configuration parameters for the model.

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ODSC - Open Data Science

SEPTEMBER 17, 2024

Fine Tuning Strategies for Language Models and Large Language Models Kevin Noel | AI Lead at Uzabase Speeda | Uzabase Japan-US Language Models (LM) and Large Language Models (LLM) have proven to have applications across many industries. This talk provides a comprehensive framework for securing LLM applications.

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AWS Machine Learning Blog

OCTOBER 2, 2024

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

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The MLOps Blog

JUNE 27, 2023

When thinking about a tool for metadata storage and management, you should consider: General business-related items : Pricing model, security, and support. When thinking about a tool for metadata storage and management, you should consider: General business-related items : Pricing model, security, and support. Can you compare images?

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Unite.AI

JANUARY 23, 2024

Large language models (LLMs) have exploded in popularity over the last few years, revolutionizing natural language processing and AI. From chatbots to search engines to creative writing aids, LLMs are powering cutting-edge applications across industries.

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AWS Machine Learning Blog

JUNE 25, 2024

With Amazon Bedrock, developers can experiment, evaluate, and deploy generative AI applications without worrying about infrastructure management. Its enterprise-grade security, privacy controls, and responsible AI features enable secure and trustworthy generative AI innovation at scale. The Step Functions workflow starts.

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AWS Machine Learning Blog

AUGUST 5, 2024

Finding relevant content usually requires searching through text-based metadata such as timestamps, which need to be manually added to these files. Next, Knowledge Bases for Amazon Bedrock augments the user’s original query with these results to a prompt, which is sent to the large language model (LLM).

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AWS Machine Learning Blog

MAY 14, 2024

A second real-time human workflow is initiated as decided by the LLM. We use a simple notification workflow in this post, but you can use any real-time human workflow to take over the AI-human conversation. We also use the same LLM to respond to this internal sentiment prompt check for simplicity.

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AWS Machine Learning Blog

SEPTEMBER 26, 2024

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 through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

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AWS Machine Learning Blog

JUNE 20, 2024

It allows LLMs to reference authoritative knowledge bases or internal repositories before generating responses, producing output tailored to specific domains or contexts while providing relevance, accuracy, and efficiency. Generation is the process of generating the final response from the LLM.

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AWS Machine Learning Blog

MAY 22, 2024

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 through a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.

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John Snow Labs

FEBRUARY 18, 2025

Combining healthcare-specific LLMs along with a terminology service and scalable data ingestion pipelines, it excels in complex queries and is ideal for organizations seeking OMOP data enrichment.

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AWS Machine Learning Blog

MAY 21, 2024

The solution uses Amazon Bedrock , a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies, providing a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

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ODSC - Open Data Science

FEBRUARY 25, 2025

This track will cover the latest best practices for managing AI models from development to deployment. Responsible AI TrackBuild Ethical, Fair, and SafeAI As AI systems become more powerful, ensuring their responsible development is more critical than ever.

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AWS Machine Learning Blog

SEPTEMBER 20, 2024

required=True, ) }, ), ] ), After the Amazon Bedrock agent determines the API operation that it needs to invoke in an action group, it sends information alongside relevant metadata as an input event to the Lambda function.

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Snorkel AI

MARCH 2, 2023

Ali Arsanjani, director of cloud partner engineering at Google Cloud , presented a talk entitled “Challenges and Ethics of DLM and LLM Adoption in the Enterprise” at Snorkel AI’s recent Foundation Model Virtual Summit. Below follows a transcript of his talk, lightly edited for readability.

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Snorkel AI

MARCH 2, 2023

Ali Arsanjani, director of cloud partner engineering at Google Cloud , presented a talk entitled “Challenges and Ethics of DLM and LLM Adoption in the Enterprise” at Snorkel AI’s recent Foundation Model Virtual Summit. Below follows a transcript of his talk, lightly edited for readability.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

Amazon SageMaker Clarify now provides AWS customers with foundation model (FM) evaluations, a set of capabilities designed to evaluate and compare model quality and responsibility metrics for any LLM, in minutes. You can use FMEval to evaluate AWS-hosted LLMs such as Amazon Bedrock, Jumpstart and other SageMaker models.

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Flipboard

DECEMBER 3, 2024

Agent architecture The following diagram illustrates the serverless agent architecture with standard authorization and real-time interaction, and an LLM agent layer using Amazon Bedrock Agents for multi-knowledge base and backend orchestration using API or Python executors. Domain-scoped agents enable code reuse across multiple agents.

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Unite.AI

AUGUST 8, 2024

While single models are suitable in some scenarios, acting as co-pilots, agentic architectures open the door for LLMs to become active components of business process automation. As such, enterprises should consider leveraging LLM-based multi-agent (LLM-MA) systems to streamline complex business processes and improve ROI.

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AWS Machine Learning Blog

JANUARY 7, 2025

Generative AI applications should be developed with adequate controls for steering the behavior of FMs. Responsible AI considerations such as privacy, security, safety, controllability, fairness, explainability, transparency and governance help ensure that AI systems are trustworthy.

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