Tag: Amazon SageMaker

In this post, we guide you through the phases of customizing SLMs on AWS, with a specific focus on automotive terminology for diagnostics as a Q&A task. We begin with the data analysis phase and progress through the end-to-end process, covering fine-tuning, deployment, and evaluation. We compare a customized SLM with a general purpose LLM, using various metrics to assess vocabulary richness and overall accuracy.

In this post, we show you how to build a generative AI-powered trip-planning service that revolutionizes the way travelers discover and explore destinations. By using advanced AI technology and Amazon Location Service, the trip planner lets users translate inspiration into personalized travel itineraries. This innovative service goes beyond traditional trip planning methods, offering real-time interaction through a chat-based interface and maintaining scalability, reliability, and data security through AWS native services.

In this post, we demonstrate how to fine-tune Meta’s latest Llama 3.2 text generation models, Llama 3.2 1B and 3B, using Amazon SageMaker JumpStart for domain-specific applications. By using the pre-built solutions available in SageMaker JumpStart and the customizable Meta Llama 3.2 models, you can unlock the models’ enhanced reasoning, code generation, and instruction-following capabilities to tailor them for your unique use cases.

This post is cowritten with Mones Raslan, Ravi Sharma and Adele Gouttes from Zalando. Zalando SE is one of Europe’s largest ecommerce fashion retailers with around 50 million active customers. Zalando faces the challenge of regular (weekly or daily) discount steering for more than 1 million products, also referred to as markdown pricing. Markdown pricing is […]

In this post, we demonstrate the power of SageMaker geospatial capabilities by mapping the world’s vegetation in under 20 minutes. This example not only highlights the efficiency of SageMaker, but also its impact how geospatial ML can be used to monitor the environment for sustainability and conservation purposes.

In this post, we show how the SageMaker Core SDK simplifies the developer experience while providing API for seamlessly executing various steps in a general ML lifecycle. We also discuss the main benefits of using this SDK along with sharing relevant resources to learn more about this SDK.

In this post, we provide a step-by-step approach of fine-tuning a Mistral model using SageMaker and import it into Amazon Bedrock using the Custom Import Model feature.

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.

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.

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.