AI accelerates problem-solving in complex scenarios
A new, data-driven approach could lead to better solutions for tricky optimization problems like global package routing or power grid operation.
How Getir reduced model training durations by 90% with Amazon SageMaker and AWS Batch | Amazon Web Services
This is a guest post co-authored by Nafi Ahmet Turgut, Hasan Burak Yel, and Damla Şentürk from Getir. Established in 2015, Getir has positioned itself as the trailblazer in the…
Boosting developer productivity: How Deloitte uses Amazon SageMaker Canvas for no-code/low-code machine learning | Amazon Web Services
The ability to quickly build and deploy machine learning (ML) models is becoming increasingly important in today’s data-driven world. However, building ML models requires significant time, effort, and specialized expertise.…
Amazon SageMaker simplifies setting up SageMaker domain for enterprises to onboard their users to SageMaker | Amazon Web Services
As organizations scale the adoption of machine learning (ML), they are looking for efficient and reliable ways to deploy new infrastructure and onboard teams to ML environments. One of the…
Experience the new and improved Amazon SageMaker Studio | Amazon Web Services
Launched in 2019, Amazon SageMaker Studio provides one place for all end-to-end machine learning (ML) workflows, from data preparation, building and experimentation, training, hosting, and monitoring. As we continue to…
Welcome to a New Era of Building in the Cloud with Generative AI on AWS | Amazon Web Services
We believe generative AI has the potential over time to transform virtually every customer experience we know. The number of companies launching generative AI applications on AWS is substantial and…
Package and deploy classical ML and LLMs easily with Amazon SageMaker, part 2: Interactive User Experiences in SageMaker Studio | Amazon Web Services
Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at scale. SageMaker makes it…
Package and deploy classical ML and LLMs easily with Amazon SageMaker, part 1: PySDK Improvements | Amazon Web Services
Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and effortlessly build, train, and deploy machine learning (ML) models at any scale. SageMaker makes…
New – Code Editor, based on Code-OSS VS Code Open Source now available in Amazon SageMaker Studio | Amazon Web Services
Today, we are excited to announce support for Code Editor, a new integrated development environment (IDE) option in Amazon SageMaker Studio. Code Editor is based on Code-OSS, Visual Studio Code…
Scale foundation model inference to hundreds of models with Amazon SageMaker – Part 1 | Amazon Web Services
As democratization of foundation models (FMs) becomes more prevalent and demand for AI-augmented services increases, software as a service (SaaS) providers are looking to use machine learning (ML) platforms that…