Category: Amazon SageMaker

In this post, we explore the new features of Model Registry that streamline foundation model (FM) management: you can now register unzipped model artifacts and pass an End User License Agreement (EULA) acceptance flag without needing users to intervene.

In this post, we show you how Thomson Reuters Labs (TR Labs) was able to develop an efficient, flexible, and powerful MLOps process by adopting a standardized MLOps framework that uses AWS SageMaker, SageMaker Experiments, SageMaker Model Registry, and SageMaker Pipelines. The goal being to accelerate how quickly teams can experiment and innovate using AI and machine learning (ML)—whether using natural language processing (NLP), generative AI, or other techniques. We discuss how this has helped decrease the time to market for fresh ideas and helped build a cost-efficient machine learning lifecycle.

In this post, we explore how to build a scalable and efficient Retrieval Augmented Generation (RAG) system using the new EMR Serverless integration, Spark’s distributed processing, and an Amazon OpenSearch Service vector database powered by the LangChain orchestration framework. This solution enables you to process massive volumes of textual data, generate relevant embeddings, and store them in a powerful vector database for seamless retrieval and generation.

In this post, we explore a solution that uses the vector engine ChromaDB and Meta Llama 3, a publicly available foundation model hosted on SageMaker JumpStart, for a Text-to-SQL use case. We shared a brief history of Meta Llama 3, best practices for prompt engineering with Meta Llama 3 models, and an architecture pattern using few-shot prompting and RAG to extract the relevant schemas stored as vectors in ChromaDB.

In this post, we provide a walkthrough of how customers can use generative artificial intelligence (AI) models and LLMs using NVIDIA NIM integration with SageMaker. We demonstrate how this integration works and how you can deploy these state-of-the-art models on SageMaker, optimizing their performance and cost.

In this post, we show how you can recommend breaking news to a user using AWS AI/ML services. By taking advantage of the power of Amazon Personalize and Amazon Titan Text Embeddings on Amazon Bedrock, you can show articles to interested users within seconds of them being published.

Today, we are excited to announce that the Snowflake Arctic Instruct model is available through Amazon SageMaker JumpStart to deploy and run inference. In this post, we walk through how to discover and deploy the Snowflake Arctic Instruct model using SageMaker JumpStart, and provide example use cases with specific prompts.

In this post, we show you how to convert Python code that fine-tunes a generative AI model in Amazon Bedrock from local files to a reusable workflow using Amazon SageMaker Pipelines decorators.

Fine-tuning Meta Llama 3.1 models with Amazon SageMaker JumpStart enables developers to customize these publicly available foundation models (FMs). The Meta Llama 3.1 collection represents a significant advancement in the field of generative artificial intelligence (AI), offering a range of capabilities to create innovative applications. The Meta Llama 3.1 models come in various sizes, with 8 billion, 70 billion, and 405 billion parameters, catering to diverse project needs. In this post, we demonstrate how to fine-tune Meta Llama 3-1 pre-trained text generation models using SageMaker JumpStart.

Amazon DataZone is a data management service that makes it quick and convenient to catalog, discover, share, and govern data stored in AWS, on-premises, and third-party sources. Amazon DataZone allows you to create and manage data zones, which are virtual data lakes that store and process your data, without the need for extensive coding or […]