Category: Amazon SageMaker

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to quickly build, train, and deploy machine learning (ML) models at scale. Amazon SageMaker removes the heavy lifting from each step of the ML process to make it easier to develop high-quality models. You can one-click deploy your […]

Amazon SageMaker Studio notebooks and Amazon SageMaker notebook instances are internet-enabled by default. However, many regulated industries, such as financial industries, healthcare, telecommunications, and others, require that network traffic traverses their own Amazon Virtual Private Cloud (Amazon VPC) to restrict and control which traffic can go through public internet. Although you can disable direct internet […]

Ever since Amazon SageMaker was introduced at AWS re:Invent 2017, customers have used the service to quickly and easily build and train machine learning (ML) models and directly deploy them into a production-ready hosted environment. SageMaker notebook instances provide a powerful, integrated Jupyter notebook interface for easy access to data sources for exploration and analysis. […]

One of the challenges in data science is getting access to operational or real-time data, which is often stored in operational database systems. Being able to connect data science tools to operational data easily and efficiently unleashes enormous potential for gaining insights from real-time data. In this post, we explore using Amazon SageMaker to analyze […]

February 2021 Update: Customers can now use native AWS CloudFormation code templates to model the infrastructure set up for Amazon SageMaker Studio and configure its access for users in their organizations at scale. For more information, please see the announcement post.  Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning […]

Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). With a single click, data scientists and developers can quickly spin up SageMaker Studio notebooks to explore datasets and build models. On October 27, 2020, Amazon released a custom images feature that allows you to launch SageMaker Studio notebooks with […]

Although we might think the world is already sufficiently mapped by the advent of global satellite images and street views, it’s far from complete because much of the world is still uncharted territory. Maps are designed for humans, and can’t be consumed by autonomous vehicles, which need a very different technology of maps with much […]

Rapid development of deep learning technology has produced an abundance of open-sourced, pre-trained models in computer vision and natural language processing. As a result, transfer learning has become a popular approach in deep learning. Transfer learning is a machine learning technique where a model pre-trained on one task is fine-tuned on a new task. Given […]

If you’re a Formula 1 (F1) fan, have you ever wondered why F1 teams have very different performances between qualifying and practice sessions? Why do they have multiple practice sessions in the first place? Can practice session results actually tell something about the upcoming qualifying race? In this post, we answer these questions and more. […]

October 2022: This post was reviewed and updated to include updates from Amazon SageMaker’s recently released SourceIdentity feature and renaming of AWS SSO to IAM Identity Center. Amazon SageMaker is a fully managed service that provides every machine learning (ML) developer and data scientist with the ability to build, train, and deploy ML models quickly. […]