AWS Machine Learning Blog
Tag: Technical How-to
Benchmarking customized models on Amazon Bedrock using LLMPerf and LiteLLM
This post begins a blog series exploring DeepSeek and open FMs on Amazon Bedrock Custom Model Import. It covers the process of performance benchmarking of custom models in Amazon Bedrock using popular open source tools: LLMPerf and LiteLLM. It includes a notebook that includes step-by-step instructions to deploy a DeepSeek-R1-Distill-Llama-8B model, but the same steps apply for any other model supported by Amazon Bedrock Custom Model Import.
Import a question answering fine-tuned model into Amazon Bedrock as a custom model
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.
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.
Enable intelligent decision-making with Amazon SageMaker Canvas and Amazon QuickSight
Every company, regardless of its size, wants to deliver the best products and services to its customers. To achieve this, companies want to understand industry trends and customer behavior, and optimize internal processes and data analyses on a routine basis. This is a crucial component of a company’s success. A very prominent part of the […]