Category: Amazon SageMaker

In this post, we show how to train, deploy, and predict natural disaster damage with Amazon SageMaker with geospatial capabilities. We use the new SageMaker geospatial capabilities to generate new inference data to test the model. Many government and humanitarian organizations need quick and accurate situational awareness when a disaster strikes. Knowing the severity, cause, […]

Amazon SageMaker Autopilot automatically builds, trains, and tunes the best machine learning (ML) models based on your data, while allowing you to maintain full control and visibility. Autopilot can also deploy trained models to real-time inference endpoints automatically. If you have workloads with spiky or unpredictable traffic patterns that can tolerate cold starts, then deploying […]

The success of any machine learning (ML) pipeline depends not just on the quality of model used, but also the ability to train and iterate upon this model. One of the key ways to improve an ML model is by choosing better tunable parameters, known as hyperparameters. This is known as hyperparameter optimization (HPO). However, […]

Amazon SageMaker JumpStart is the Machine Learning (ML) hub of SageMaker providing pre-trained, publicly available models for a wide range of problem types to help you get started with machine learning. JumpStart also offers example notebooks that use Amazon SageMaker features like spot instance training and experiments over a large variety of model types and […]

According to a 2020 survey of data scientists conducted by Anaconda, data preparation is one of the critical steps in machine learning (ML) and data analytics workflows, and often very time consuming for data scientists. Data scientists spend about 66% of their time on data preparation and analysis tasks, including loading (19%), cleaning (26%), and […]

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 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 […]

Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). It provides a single, web-based visual interface where you can perform all ML development steps, including preparing data and building, training, and deploying models. Within an Amazon SageMaker Domain, users can provision a personal Amazon SageMaker Studio IDE application, which […]

Amazon SageMaker now allows you to compare the performance of a new version of a model serving stack with the currently deployed version prior to a full production rollout using a deployment safety practice known as shadow testing. Shadow testing can help you identify potential configuration errors and performance issues before they impact end-users. With […]

As companies are increasingly adopting machine learning (ML) for their mainstream enterprise applications, more of their business decisions are influenced by ML models. As a result of this, having simplified access control and enhanced transparency across all your ML models makes it easier to validate that your models are performing well and take action when […]

Administrators of machine learning (ML) workloads are focused on ensuring that users are operating in the most secure manner, striving towards a principal of least privilege design. They have a wide variety of personas to account for, each with their own unique sets of needs, and building the right sets of permissions policies to meet […]