Damage assessment using Amazon SageMaker geospatial capabilities and custom SageMaker models 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, […]
https://aws.amazon.com/blogs/m....achine-learning/dama

Exafunction supports AWS Inferentia to unlock best price performance for machine learning inference Across all industries, machine learning (ML) models are getting deeper, workflows are getting more complex, and workloads are operating at larger scales. Significant effort and resources are put into making these models more accurate since this investment directly results in better products and experiences. On the other hand, making these models run efficiently in production […]
https://aws.amazon.com/blogs/m....achine-learning/exaf

Prepare data from Amazon EMR for machine learning using Amazon SageMaker Data Wrangler Data preparation is a principal component of machine learning (ML) pipelines. In fact, it is estimated that data professionals spend about 80 percent of their time on data preparation. In this intensive competitive market, teams want to analyze data and extract more meaningful insights quickly. Customers are adopting more efficient and visual ways to build […]
https://aws.amazon.com/blogs/m....achine-learning/prep

Deploy Amazon SageMaker Autopilot models to serverless inference endpoints 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 […]
https://aws.amazon.com/blogs/m....achine-learning/depl

Use your own training scripts and automatically select the best model using hyperparameter optimization in Amazon SageMaker 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, […]
https://aws.amazon.com/blogs/m....achine-learning/use-