Tag: Antje Barth

In 2019, we introduced Amazon SageMaker Studio, the first fully integrated development environment (IDE) for data science and machine learning (ML). SageMaker Studio gives you access to fully managed Jupyter Notebooks that integrate with purpose-built tools to perform all ML steps, from preparing data to training and debugging models, tracking experiments, deploying and monitoring models, and managing pipelines. Today, I’m excited to announce the next generation of Amazon SageMaker Notebooks to increase efficiency across the ML development workflow. You can now improve data quality in minutes with the built-in data preparation capability, edit the same notebooks with your teams in real time, and automatically convert notebook code to production-ready jobs. Let me show you what’s new! New Notebook Capability for…

Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. SageMaker JumpStart gives you access to built-in algorithms with pre-trained models from popular model hubs, pre-trained foundation models to help you perform tasks such as article summarization and image generation, and end-to-end solutions to solve common use cases. Today, I’m happy to announce that you can now share ML artifacts, such as models and notebooks, more easily with other users that share your AWS account using SageMaker JumpStart. Using SageMaker JumpStart to Share ML Artifacts Machine learning is a team sport. You might want to share your models and notebooks with other data scientists in your team to collaborate and increase productivity. Or,…

As companies increasingly adopt machine learning (ML) for their business applications, they are looking for ways to improve governance of their ML projects with simplified access control and enhanced visibility across the ML lifecycle. A common challenge in that effort is managing the right set of user permissions across different groups and ML activities. For example, a data scientist in your team that builds and trains models usually requires different permissions than an MLOps engineer that manages ML pipelines. Another challenge is improving visibility over ML projects. For example, model information, such as intended use, out-of-scope use cases, risk rating, and evaluation results, is often captured and shared via emails or documents. In addition, there is often no simple mechanism…

Today, I’m excited to announce a new, redesigned user interface (UI) for Amazon SageMaker Studio. SageMaker Studio provides a single, web-based visual interface where you can perform all machine learning (ML) development steps with a comprehensive set of ML tools. For example, you can prepare data using SageMaker Data Wrangler, build ML models with fully managed Jupyter notebooks, and deploy models using SageMaker’s multi-model endpoints. Introducing the Redesigned UI for Amazon SageMaker Studio The redesigned UI makes it easier for you to discover and get started with the ML tools in SageMaker Studio. One highlight of the new UI includes a redesigned navigation menu with links to SageMaker capabilities that follow the typical ML development workflow from preparing data to…