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streaming_events
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README.md
README.md
 
 

README.md

Amazon Personalize Operations

This topic contains examples on the following topics:

  • Maintaining Personalized Experiences with Machine Learning

    • This AWS Solution allows you to automate the end-to-end process of importing datasets, creating solutions and solution versions, creating and updating campaigns, creating filters, and running batch inference jobs. These processes can be run on-demand or triggered based on a schedule that you define.

  • MLOps (legacy)

  • Data Science SDK

    • This is a project to showcase how to quickly deploy a Personalize Campaign in a fully automated fashion using AWS Data Science SDK. To get started navigate to the ml_ops_ds_sdk folder and follow the README instructions.

  • Personalization APIs

    • Real-time low latency API framework that sits between your applications and recommender systems such as Amazon Personalize. Provides best practice implementations of response caching, API gateway configurations, A/B testing with Amazon CloudWatch Evidently, inference-time item metadata, automatic contextual recommendations, and more.

  • Lambda Examples

    • This folder starts with a basic example of integrating put_events into your Personalize Campaigns by using Lambda functions processing new data from S3. To get started navigate to the lambda_examples folder and follow the README instructions.

  • Streaming Events

    • This is a project to showcase how to quickly deploy an API Layer infront of your Amazon Personalize Campaign and your Event Tracker endpoint. To get started navigate to the streaming_events folder and follow the README instructions.

  • Personalize Monitor

    • This project adds monitoring, alerting, a dashboard, and optimization tools for running Amazon Personalize across your AWS environments.

License Summary

This sample code is made available under a modified MIT license. See the LICENSE file.