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AWS

The sections described below provide techniques for integrating Amazon Web Services with DataRobot.

Topic Describes...
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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