How Rocket Companies modernized their data science solution on AWS
In this post, we share how we modernized Rocket Companies’ data science solution on AWS to increase the speed to delivery from eight weeks to under one hour, improve operational…
Amazon SageMaker Studio
Streamline custom environment provisioning for Amazon SageMaker Studio: An automated CI/CD pipeline approach
In this post, we show how to create an automated continuous integration and delivery (CI/CD) pipeline solution to build, scan, and deploy custom Docker images to SageMaker Studio domains. You…
How Northpower used computer vision with AWS to automate safety inspection risk assessments
In this post, we share how Northpower has worked with their technology partner Sculpt to reduce the effort and carbon required to identify and remediate public safety risks. Specifically, we…
Improve RAG accuracy with fine-tuned embedding models on Amazon SageMaker
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…
Explore data with ease: Use SQL and Text-to-SQL in Amazon SageMaker Studio JupyterLab notebooks
Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. In the process of working on their ML tasks,…
Advanced RAG patterns on Amazon SageMaker
Today, customers of all industries—whether it’s financial services, healthcare and life sciences, travel and hospitality, media and entertainment, telecommunications, software as a service (SaaS), and even proprietary model providers—are using…
Automate Amazon SageMaker Pipelines DAG creation
Creating scalable and efficient machine learning (ML) pipelines is crucial for streamlining the development, deployment, and management of ML models. In this post, we present a framework for automating the…
Supercharge your AI team with Amazon SageMaker Studio: A comprehensive view of Deutsche Bahn’s AI platform transformation
AI’s growing influence in large organizations brings crucial challenges in managing AI platforms. These include developing a scalable and operationally efficient platform that adheres to organizational compliance and security standards.…
How Axfood enables accelerated machine learning throughout the organization using Amazon SageMaker
This is a guest post written by Axfood AB. In this post, we share how Axfood, a large Swedish food retailer, improved operations and scalability of their existing artificial intelligence…
Reduce inference time for BERT models using neural architecture search and SageMaker Automated Model Tuning
In this post, we demonstrate how to use neural architecture search (NAS) based structural pruning to compress a fine-tuned BERT model to improve model performance and reduce inference times. Pre-trained…