The sections described below provide techniques for integrating Amazon Web Services with DataRobot.
|Import data from AWS S3||Importing data from AWS S3 to AI Catalog and creating an ML project.|
|Deploy models on EKS||Deploying and monitor DataRobot models on AWS Elastic Kubernetes Service (EKS) clusters.|
|Serverless MLOps agents||Monitoring external models with serverless MLOps agents.|
|Path-based routing to PPS on AWS||Using a single IP address for all Portable Prediction Servers through path-based routing.|
|Score Snowflake data on AWS EMR Spark||Scoring Snowflake data via DataRobot models on AWS Elastic Map Reduce (EMR) Spark.|
|AWS Lambda reporting to MLOps||AWS Lambda serverless reporting of actuals to DataRobot MLOps.|
|Use DataRobot Prime models with AWS Lambda||Using DataRobot Prime models with AWS Lambda.|
|Use Scoring Code with AWS Lambda||Making predictions using Scoring Code deployed on AWS Lambda.|
|Deploy models on Sagemaker||Deploying on SageMaker and monitoring with MLOps agents.|
|Use Scoring Code with AWS SageMaker||Making predictions using Scoring Code deployed on AWS SageMaker.|
|Monitor SageMaker models in MLOps||Monitoring a SageMaker model that has been deployed to AWS for real-time scoring in DataRobot MLOps.|
|Ingest data with AWS Athena||Ingesting AWS Athena and Parquet data for machine learning.|
Updated December 28, 2022
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