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Homepage » Amazon Web Services Solutions » Machine Learning » Recommender System Solution

Recommender System Solution

Quickly build a Cloud-Native, End-to-End recommendation system with flexible architecture, and engineer AI theory around the enterprise's business use cases.

Contact Sales

Homepage » Amazon Web Services Solutions » Machine Learning » Recommender System Solution

Recommender System Solution

Quickly build a Cloud-Native, End-to-End recommendation system with flexible architecture, and engineer AI theory around the enterprise's business use cases.

Contact Sales

Overview

Building a recommender systems is a challenging task as it requires specialized expertise in analytics, machine learning, software engineering, and systems operations, etc. Amazon Web Services provides an end-to-end, cloud-native recommendation system solution. The solution leveraging services such as Amazon SageMaker, Amazon Elastic Kubernetes Service(Amazon EKS), Amazon CodeBuild, etc, allows you to significantly shorten the time to design and build your own recommender system. The solution provides different recommendation algorithms, including the built-in algorithms of Amazon Personalize . You can easily switch between different recommendation methods, compare the recommendation results and choose the most suitable one for your business.

The solution is based on Microservice Architecture, which provides you the flexibility of building recommender system for various scenarios (for example, news recommendation, movie recommendation, course recommendation, etc.) .

Benefits

Plugins

This solution offers built-in scenarios for recommendation (news and movie). The core services are designed as plugins enabling you to customize for other scenarios.

Plugins

This solution offers built-in scenarios for recommendation (news and movie). The core services are designed as plugins enabling you to customize for other scenarios.

Scalability

Amazon Elastic Kubernetes Service (Amazon EKS) gives you the flexibility to start, run, and scale Kubernetes applications in the cloud.

Scalability

Amazon Elastic Kubernetes Service (Amazon EKS) gives you the flexibility to start, run, and scale Kubernetes applications in the cloud.

Multiple Recommendation methods for your choice

This solution provides in total four recommendation methods, each of which contains different algorithm models and logics. You can switch to different methods, compare their recommendation results, and find the best method for your own business logic.

Multiple Recommendation methods for your choice

End-to-End

The solution contains all the basic components of a recommender system, allowing you to quickly build a complete recommender system. This solution also integrates the automated CI/CD pipeline which makes it easy for developers to release new implementations.

End-to-End

Amazon Web Services Solution Overview

The diagram below presents the architecture of the solution. You can contact sales for deployment materials and get supports for customization.

The solution will build a cloud-native recommender system for you from three aspects:

Architecture of Serverless Image Handler

STEP 1

Online service module. The solution leverages Amazon EKS to provide a set of microservices including user portrait, data loader, event notification, recall, rank, filter and so on. These services will interact with Amazon Redis.

STEP 2

Offline processing module. The solution leverages Amazon Step Functions and Amazon SageMaker to implement the offline processing process, which includes data pre-processing, model training, model validation, batch processing and so on. Whenever new files or models are generated, the solution will notice the online services reload them.

STEP 3

Automated release pipeline. The solution leverages Amazon CodeBuild and Argo CD to provide the CI/CD, which enables you to easily deploy the latest software into the production environment.

This solution also supports three recommendation methods of Amazon Personalize, which can be respectively used in services such as recall, sorting, and events. The customer can easily switch between different recommendation methods.