AWS Machine Learning Blog

Tag: Amazon SageMaker

Deploy generative AI agents in your contact center for voice and chat using Amazon Connect, Amazon Lex, and Amazon Bedrock Knowledge Bases

Deploy generative AI agents in your contact center for voice and chat using Amazon Connect, Amazon Lex, and Amazon Bedrock Knowledge Bases

In this post, we show you how DoorDash built a generative AI agent using Amazon Connect, Amazon Lex, and Amazon Bedrock Knowledge Bases to provide a low-latency, self-service experience for their delivery workers.

Accelerate pre-training of Mistral’s Mathstral model with highly resilient clusters on Amazon SageMaker HyperPod

In this post, we present to you an in-depth guide to starting a continual pre-training job using PyTorch Fully Sharded Data Parallel (FSDP) for Mistral AI’s Mathstral model with SageMaker HyperPod.

Deploy Amazon SageMaker pipelines using AWS Controllers for Kubernetes

Deploy Amazon SageMaker pipelines using AWS Controllers for Kubernetes

In this post, we show how ML engineers familiar with Jupyter notebooks and SageMaker environments can efficiently work with DevOps engineers familiar with Kubernetes and related tools to design and maintain an ML pipeline with the right infrastructure for their organization. This enables DevOps engineers to manage all the steps of the ML lifecycle with the same set of tools and environment they are used to.

Effectively manage foundation models for generative AI applications with Amazon SageMaker Model Registry

Effectively manage foundation models for generative AI applications with Amazon SageMaker Model Registry

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.

How Thomson Reuters Labs achieved AI/ML innovation at pace with AWS MLOps services

How Thomson Reuters Labs achieved AI/ML innovation at pace with AWS MLOps services

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.

Provide a personalized experience for news readers using Amazon Personalize and Amazon Titan Text Embeddings on Amazon Bedrock

Provide a personalized experience for news readers using Amazon Personalize and Amazon Titan Text Embeddings on Amazon Bedrock

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.

MusicGen on Amazon SageMaker Asynchronous Inference

Inference AudioCraft MusicGen models using Amazon SageMaker

Music generation models have emerged as powerful tools that transform natural language text into musical compositions. Originating from advancements in artificial intelligence (AI) and deep learning, these models are designed to understand and translate descriptive text into coherent, aesthetically pleasing music. Their ability to democratize music production allows individuals without formal training to create high-quality […]

Import a fine-tuned Meta Llama 3 model for SQL query generation on Amazon Bedrock

In this post, we demonstrate the process of fine-tuning Meta Llama 3 8B on SageMaker to specialize it in the generation of SQL queries (text-to-SQL). Meta Llama 3 8B is a relatively small model that offers a balance between performance and resource efficiency. AWS customers have explored fine-tuning Meta Llama 3 8B for the generation of SQL queries—especially when using non-standard SQL dialects—and have requested methods to import their customized models into Amazon Bedrock to benefit from the managed infrastructure and security that Amazon Bedrock provides when serving those models.

Solution architecture

Monks boosts processing speed by four times for real-time diffusion AI image generation using Amazon SageMaker and AWS Inferentia2

This post is co-written with Benjamin Moody from Monks. Monks is the global, purely digital, unitary operating brand of S4Capital plc. With a legacy of innovation and specialized expertise, Monks combines an extraordinary range of global marketing and technology services to accelerate business possibilities and redefine how brands and businesses interact with the world. Its […]