Tag: Amazon SageMaker Studio
Accelerating AI/ML development at BMW Group with Amazon SageMaker Studio
This post is co-written with Marc Neumann, Amor Steinberg and Marinus Krommenhoek from BMW Group. The BMW Group – headquartered in Munich, Germany – is driven by [more…]
Explore advanced techniques for hyperparameter optimization with Amazon SageMaker Automatic Model Tuning
Creating high-performance machine learning (ML) solutions relies on exploring and optimizing training parameters, also known as hyperparameters. Hyperparameters are the knobs and levers that we use to [more…]
Implement fine-grained access control in Amazon SageMaker Studio and Amazon EMR using Apache Ranger and Microsoft Active Directory
Amazon SageMaker Studio is a fully integrated development environment (IDE) for machine learning (ML) that enables data scientists and developers to perform every step of the ML [more…]
Create, train, and deploy Amazon Redshift ML model integrating features from Amazon SageMaker Feature Store
Amazon Redshift is a fast, petabyte-scale, cloud data warehouse that tens of thousands of customers rely on to power their analytics workloads. Data analysts and database developers [more…]
Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker
Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have [more…]
How VirtuSwap accelerates their pandas-based trading simulations with an Amazon SageMaker Studio custom container and AWS GPU instances
This post is written in collaboration with Dima Zadorozhny and Fuad Babaev from VirtuSwap. VirtuSwap is a startup company developing innovative technology for decentralized exchange of assets on [more…]
Amazon SageMaker simplifies the Amazon SageMaker Studio setup for individual users
Today, we are excited to announce the simplified Quick setup experience in Amazon SageMaker. With this new capability, individual users can launch Amazon SageMaker Studio with default [more…]
Amazon SageMaker Domain in VPC only mode to support SageMaker Studio with auto shutdown Lifecycle Configuration and SageMaker Canvas with Terraform
Amazon SageMaker Domain supports SageMaker machine learning (ML) environments, including SageMaker Studio and SageMaker Canvas. SageMaker Studio is a fully integrated development environment (IDE) that provides a [more…]
Automatically generate impressions from findings in radiology reports using generative AI on AWS
Radiology reports are comprehensive, lengthy documents that describe and interpret the results of a radiological imaging examination. In a typical workflow, the radiologist supervises, reads, and interprets [more…]
Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift
Amazon Redshift is the most popular cloud data warehouse that is used by tens of thousands of customers to analyze exabytes of data every day. Many practitioners [more…]
Host the Spark UI on Amazon SageMaker Studio
Amazon SageMaker offers several ways to run distributed data processing jobs with Apache Spark, a popular distributed computing framework for big data processing. You can run Spark [more…]
Bring your own AI using Amazon SageMaker with Salesforce Data Cloud
This post is co-authored by Daryl Martis, Director of Product, Salesforce Einstein AI. We’re excited to announce Amazon SageMaker and Salesforce Data Cloud integration. With this capability, [more…]
Build a personalized avatar with generative AI using Amazon SageMaker
Generative AI has become a common tool for enhancing and accelerating the creative process across various industries, including entertainment, advertising, and graphic design. It enables more personalized [more…]
Build protein folding workflows to accelerate drug discovery on Amazon SageMaker
Drug development is a complex and long process that involves screening thousands of drug candidates and using computational or experimental methods to evaluate leads. According to McKinsey, [more…]
Use Stable Diffusion XL with Amazon SageMaker JumpStart in Amazon SageMaker Studio
Today we are excited to announce that Stable Diffusion XL 1.0 (SDXL 1.0) is available for customers through Amazon SageMaker JumpStart. SDXL 1.0 is the latest image [more…]
Configure cross-account access of Amazon Redshift clusters in Amazon SageMaker Studio using VPC peering
With cloud computing, as compute power and data became more available, machine learning (ML) is now making an impact across every industry and is a core part [more…]
Access private repos using the @remote decorator for Amazon SageMaker training workloads
As more and more customers are looking to put machine learning (ML) workloads in production, there is a large push in organizations to shorten the development lifecycle [more…]
Four approaches to manage Python packages in Amazon SageMaker Studio notebooks
This post presents and compares options and recommended practices on how to manage Python packages and virtual environments in Amazon SageMaker Studio notebooks. A public GitHub repo [more…]
Boomi uses BYOC on Amazon 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 [more…]
Run notebooks as batch jobs in Amazon SageMaker Studio Lab
Recently, the Amazon SageMaker Studio launched an easy way to run notebooks as batch jobs that can run on a recurring schedule. Amazon SageMaker Studio Lab also [more…]
Operationalize your Amazon SageMaker Studio notebooks as scheduled notebook jobs
Amazon 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 [more…]
