AWS Machine Learning Blog
Tag: HAQM SageMaker Studio
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 stability and support by reducing incident tickets by over 99% in 18 months, power 10 million automated data science and AI decisions made daily, and provide a seamless data science development experience.
Falcon 3 models now available in HAQM SageMaker JumpStart
We are excited to announce that the Falcon 3 family of models from TII are available in HAQM SageMaker JumpStart. In this post, we explore how to deploy this model efficiently on HAQM SageMaker AI.
Improve RAG accuracy with fine-tuned embedding models on HAQM SageMaker
This post demonstrates how to use HAQM SageMaker to fine tune a Sentence Transformer embedding model and deploy it with an HAQM SageMaker Endpoint. The code from this post and more examples are available in the GitHub repo.
Four approaches to manage Python packages in HAQM SageMaker Studio notebooks
This post presents and compares options and recommended practices on how to manage Python packages and virtual environments in HAQM SageMaker Studio notebooks. A public GitHub repo provides hands-on examples for each of the presented approaches. HAQM SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, […]
Boomi uses BYOC on HAQM SageMaker Studio to scale custom Markov chain implementation
This post is co-written with Swagata Ashwani, Senior Data Scientist at Boomi. Boomi is an enterprise-level software as a service (SaaS) independent software vendor (ISV) that creates developer enablement tooling for software engineers. These tools integrate via API into Boomi’s core service offering. In this post, we discuss how Boomi used the bring-your-own-container (BYOC) approach […]
Run notebooks as batch jobs in HAQM SageMaker Studio Lab
Recently, the HAQM SageMaker Studio launched an easy way to run notebooks as batch jobs that can run on a recurring schedule. HAQM SageMaker Studio Lab also supports this feature, enabling you to run notebooks that you develop in SageMaker Studio Lab in your AWS account. This enables you to quickly scale your machine learning […]
Operationalize your HAQM SageMaker Studio notebooks as scheduled notebook jobs
HAQM SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. In addition to the interactive ML experience, data workers also seek solutions to run notebooks as ephemeral jobs without the need to refactor code as Python modules or learn DevOps tools and best practices […]
Use HAQM SageMaker Data Wrangler in HAQM SageMaker Studio with a default lifecycle configuration
If you use the default lifecycle configuration for your domain or user profile in HAQM SageMaker Studio and use HAQM SageMaker Data Wrangler for data preparation, then this post is for you. In this post, we show how you can create a Data Wrangler flow and use it for data preparation in a Studio environment […]
Build, Share, Deploy: how business analysts and data scientists achieve faster time-to-market using no-code ML and HAQM SageMaker Canvas
April 2023: This post was reviewed and updated with HAQM SageMaker Canvas’s new features and UI changes. Machine learning (ML) helps organizations increase revenue, drive business growth, and reduce cost by optimizing core business functions across multiple verticals, such as demand forecasting, credit scoring, pricing, predicting customer churn, identifying next best offers, predicting late shipments, […]