AWS Machine Learning Blog

MAY 13, 2024

In part 1 of this blog series, we discussed how a large language model (LLM) available on Amazon SageMaker JumpStart can be fine-tuned for the task of radiology report impression generation. Amazon Bedrock also comes with a broad set of capabilities required to build generative AI applications with security, privacy, and responsible AI.

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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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ODSC - Open Data Science

FEBRUARY 25, 2025

Topics Include: Agentic AI DesignPatterns LLMs & RAG forAgents Agent Architectures &Chaining Evaluating AI Agent Performance Building with LangChain and LlamaIndex Real-World Applications of Autonomous Agents Who Should Attend: Data Scientists, Developers, AI Architects, and ML Engineers seeking to build cutting-edge autonomous systems.

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AWS Machine Learning Blog

AUGUST 1, 2024

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading artificial intelligence (AI) companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API. INST] Assistant: The following animation shows the results.

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ODSC - Open Data Science

JANUARY 10, 2024

The AI Paradigm Shift: Under the Hood of a Large Language Models Valentina Alto | Azure Specialist — Data and Artificial Intelligence | Microsoft Develop an understanding of Generative AI and Large Language Models, including the architecture behind them, their functioning, and how to leverage their unique conversational capabilities.

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The MLOps Blog

JUNE 27, 2023

Collaborative workflows : Dataset storage and versioning tools should support collaborative workflows, allowing multiple users to access and contribute to datasets simultaneously, ensuring efficient collaboration among ML engineers, data scientists, and other stakeholders.

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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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AWS Machine Learning Blog

MAY 9, 2024

Governance Establish governance that enables the organization to scale value delivery from AI/ML initiatives while managing risk, compliance, and security. Additionally, pay special attention to the changing nature of the risk and cost that is associated with the development as well as the scaling of AI.

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AWS Machine Learning Blog

JANUARY 26, 2024

Many customers are looking for guidance on how to manage security, privacy, and compliance as they develop generative AI applications. Understanding and addressing LLM vulnerabilities, threats, and risks during the design and architecture phases helps teams focus on maximizing the economic and productivity benefits generative AI can bring.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

However, harnessing this potential while ensuring the responsible and effective use of these models hinges on the critical process of LLM evaluation. An evaluation is a task used to measure the quality and responsibility of output of an LLM or generative AI service. Who needs to perform LLM evaluation?

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AWS Machine Learning Blog

DECEMBER 17, 2024

Amazon SageMaker helps data scientists and machine learning (ML) engineers build FMs from scratch, evaluate and customize FMs with advanced techniques, and deploy FMs with fine-grain controls for generative AI use cases that have stringent requirements on accuracy, latency, and cost. Of the six challenges, the LLM met only one.

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AWS Machine Learning Blog

OCTOBER 11, 2024

This post dives deep into Amazon Bedrock Knowledge Bases , which helps with the storage and retrieval of data in vector databases for RAG-based workflows, with the objective to improve large language model (LLM) responses for inference involving an organization’s datasets. The LLM response is passed back to the agent.

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AWS Machine Learning Blog

DECEMBER 2, 2024

For LLMs that often require high throughput and low-latency inference requests, this loading process can add significant overhead to the total deployment and scaling time, potentially impacting application performance during traffic spikes. During our performance testing we were able to load the llama-3.1-70B 70B model on an ml.p4d.24xlarge

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AWS Machine Learning Blog

NOVEMBER 18, 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, Mistral AI, 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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