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AWS

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

Topic Describes...
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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