Artificial Intelligence
Author: Janisha Anand
Build and evaluate machine learning models with advanced configurations using the SageMaker Canvas model leaderboard
Amazon SageMaker Canvas is a no-code workspace that enables analysts and citizen data scientists to generate accurate machine learning (ML) predictions for their business needs. Starting today, SageMaker Canvas supports advanced model build configurations such as selecting a training method (ensemble or hyperparameter optimization) and algorithms, customizing the training and validation data split ratio, and […]
Deploy ML models built in Amazon SageMaker Canvas to Amazon SageMaker real-time endpoints
Amazon SageMaker Canvas now supports deploying machine learning (ML) models to real-time inferencing endpoints, allowing you take your ML models to production and drive action based on ML-powered insights. SageMaker Canvas is a no-code workspace that enables analysts and citizen data scientists to generate accurate ML predictions for their business needs. Until now, SageMaker Canvas […]
Retrain ML models and automate batch predictions in Amazon SageMaker Canvas using updated datasets
You can now retrain machine learning (ML) models and automate batch prediction workflows with updated datasets in Amazon SageMaker Canvas, thereby making it easier to constantly learn and improve the model performance and drive efficiency. An ML model’s effectiveness depends on the quality and relevance of the data it’s trained on. As time progresses, the […]
Operationalize ML models built in Amazon SageMaker Canvas to production using the Amazon SageMaker Model Registry
You can now register machine learning (ML) models built in Amazon SageMaker Canvas with a single click to the Amazon SageMaker Model Registry, enabling you to operationalize ML models in production. Canvas is a visual interface that enables business analysts to generate accurate ML predictions on their own—without requiring any ML experience or having to […]
Launch Amazon SageMaker Autopilot experiments directly from within Amazon SageMaker Pipelines to easily automate MLOps workflows
Amazon SageMaker Autopilot, a low-code machine learning (ML) service that automatically builds, trains, and tunes the best ML models based on tabular data, is now integrated with Amazon SageMaker Pipelines, the first purpose-built continuous integration and continuous delivery (CI/CD) service for ML. This enables the automation of an end-to-end flow of building ML models using […]
Amazon SageMaker Autopilot is up to eight times faster with new ensemble training mode powered by AutoGluon
Amazon SageMaker Autopilot has added a new training mode that supports model ensembling powered by AutoGluon. Ensemble training mode in Autopilot trains several base models and combines their predictions using model stacking. For datasets less than 100 MB, ensemble training mode builds machine learning (ML) models with high accuracy quickly—up to eight times faster than […]