AWS Machine Learning Blog

AUGUST 1, 2024

By demonstrating the process of deploying fine-tuned models, we aim to empower data scientists, ML engineers, and application developers to harness the full potential of FMs while addressing unique application requirements. AI/ML Specialist Solutions Architect working on Amazon Web Services.

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AWS Machine Learning Blog

JUNE 8, 2023

We have included a sample project to quickly deploy an Amazon Lex bot that consumes a pre-trained open-source LLM. This mechanism allows an LLM to recall previous interactions to keep the conversation’s context and pace. We also use LangChain, a popular framework that simplifies LLM-powered applications.

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AWS Machine Learning Blog

APRIL 29, 2024

In 2024, however, organizations are using large language models (LLMs), which require relatively little focus on NLP, shifting research and development from modeling to the infrastructure needed to support LLM workflows. This often means the method of using a third-party LLM API won’t do for security, control, and scale reasons.

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ODSC - Open Data Science

FEBRUARY 21, 2025

AI Agents AI Software Engineering Agents: What Works and WhatDoesnt Robert Brennan | CEO | All HandsAI AI is reshaping software development, but are autonomous coding agents like Devin and OpenHands a game-changer or just hype? Discover what they will be presenting at ODSC Eastbelow.

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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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Mlearning.ai

AUGUST 18, 2023

As a software developer the more versed you are in DevOps the better you can foresee issues, fix bugs and be a valued team member. As an ML engineer you’re in charge of some code/model. This project structure is also applicable to the new LLM world we’re all been introduced to. Same analogy applies to MLOps.

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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

DECEMBER 2, 2024

In this post, we explore the new Container Caching feature for SageMaker inference, addressing the challenges of deploying and scaling large language models (LLMs). We discuss how this innovation significantly reduces container download and load times during scaling events, a major bottleneck in LLM and generative AI inference.

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AWS Machine Learning Blog

APRIL 19, 2024

of the SageMaker ACK Operators adds support for inference components , which until now were only available through the SageMaker API and the AWS Software Development Kits (SDKs). About the Authors Rajesh Ramchander is a Principal ML Engineer in Professional Services at AWS. Release v1.2.9

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AWS Machine Learning Blog

JULY 23, 2024

collection of multilingual large language models (LLMs), which includes pre-trained and instruction tuned generative AI models in 8B, 70B, and 405B sizes, is available through Amazon SageMaker JumpStart to deploy for inference. Architecturally, the core LLM for Llama 3 and Llama 3.1 models using SageMaker JumpStart.

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AWS Machine Learning Blog

DECEMBER 4, 2024

Available in SageMaker AI and SageMaker Unified Studio (preview) Data scientists and ML engineers can access these applications from Amazon SageMaker AI (formerly known as Amazon SageMaker) and from SageMaker Unified Studio. Deepchecks Deepchecks specializes in LLM evaluation.

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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. Marc Karp is an ML Architect with the Amazon SageMaker Service team.

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