Tag: Amazon SageMaker

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.

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 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.

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 […]

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.

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 […]

This post demonstrates how to use Amazon SageMaker to fine tune a Sentence Transformer embedding model and deploy it with an Amazon SageMaker Endpoint. The code from this post and more examples are available in the GitHub repo.

Stable Diffusion XL by Stability AI is a high-quality text-to-image deep learning model that allows you to generate professional-looking images in various styles. Managed versions of Stable Diffusion XL are already available to you on Amazon SageMaker JumpStart (see Use Stable Diffusion XL with Amazon SageMaker JumpStart in Amazon SageMaker Studio) and Amazon Bedrock (see […]

Amazon SageMaker is a machine learning (ML) platform designed to simplify the process of building, training, deploying, and managing ML models at scale. With a comprehensive suite of tools and services, SageMaker offers developers and data scientists the resources they need to accelerate the development and deployment of ML solutions. In today’s fast-paced technological landscape, […]