The sections described below provide techniques for integrating Amazon Web Services with DataRobot.
|Importing data from AWS S3||Import data from AWS S3 to AI Catalog and creating an ML project.|
|Deploying models on EKS||Deploy and monitor DataRobot models on AWS Elastic Kubernetes Service (EKS) clusters.|
|Serverless MLOps agents||Monitor external models with serverless MLOps agents.|
|Path-based routing to PPS on AWS||Use a single IP address for all Portable Prediction Servers through path-based routing.|
|Scoring Snowflake data on AWS EMR Spark||Score 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.|
|Using DataRobot Prime models with AWS Lambda||Use DataRobot Prime models with AWS Lambda.|
|Using Scoring Code with AWS Lambda||Make predictions using Scoring Code deployed on AWS Lambda.|
|Deploying models on Sagemaker||Deploy on SageMaker and monitoring with MLOps agents.|
|Monitoring SageMaker models in MLOps||Monitor a SageMaker model that has been deployed to AWS for real-time scoring in DataRobot MLOps.|
|Using Scoring Code with SageMaker||Make predictions using Scoring Code deployed on AWS SageMaker.|
|Ingesting data with AWS Athena||Ingest AWS Athena and Parquet data for machine learning.|
Updated July 7, 2022
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