AWS Machine Learning Blog

Category: Amazon Personalize

The following diagram illustrates our solution architecture.

Setting up Amazon Personalize with AWS Glue

Data can be used in a variety of ways to satisfy the needs of different business units, such as marketing, sales, or product. In this post, we focus on using data to create personalized recommendations to improve end-user engagement. Most ecommerce applications consume a huge amount of customer data that can be used to provide […]

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Run the following code to trigger a recommendation workflow using the underlying Lambda function and Step Functions states:

Automating an Amazon Personalize solution using the AWS Step Functions Data Science SDK

Machine learning (ML)-based recommender systems aren’t a new concept across organizations such as retail, media and entertainment, and education, but developing such a system can be a resource-intensive task—from data labelling, training and inference, to scaling. You also need to apply continuous integration, continuous deployment, and continuous training to your ML model, or MLOps. The […]

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Helping small businesses deliver personalized experiences with the Amazon Personalize extension for Magento

This is a guest post by Jeff Finkelstein, founder of Customer Paradigm, a full-service interactive media firm and Magento solutions partner. Many small retailers use Magento, an open-source ecommerce platform, to create websites or mobile applications to sell their products online. Personalization is key to creating high-quality ecommerce experiences, but small businesses often lack access […]

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Amazon Personalize now supports dynamic filters for applying business rules to your recommendations on the fly

We’re excited to announce dynamic filters in Amazon Personalize, which allow you to apply business rules to your recommendations on the fly, without any extra cost. Dynamic filters create better user experiences by allowing you to tailor your business rules for each user when you generate recommendations. They save you time by removing the need […]

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This month in AWS Machine Learning: October edition

Every day there is something new going on in the world of AWS Machine Learning—from launches to new to use cases to interactive trainings. We’re packaging some of the not-to-miss information from the ML Blog and beyond for easy perusing each month. Check back at the end of each month for the latest roundup. Launches […]

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Amazon Personalize improvements reduce model training time by up to 40% and latency for generating recommendations by up to 30%

We’re excited to announce new efficiency improvements for Amazon Personalize. These improvements decrease the time required to train solutions (the machine learning models trained with your data) by up to 40% and reduce the latency for generating real-time recommendations by up to 30%. Amazon Personalize enables you to build applications with the same machine learning […]

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Simplify data management with new APIs in Amazon Personalize

Amazon Personalize now makes it easier to manage your growing item and user catalogs with new APIs to incrementally add items and users in your datasets to create personalized recommendations. With the new putItems and putUsers APIs, you can simplify the process of managing your datasets. You no longer need to upload an entire dataset […]

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Selecting the right metadata to build high-performing recommendation models with Amazon Personalize

In this post, we show you how to select the right metadata for your use case when building a recommendation engine using Amazon Personalize. The aim is to help you optimize your models to generate more user-relevant recommendations. We look at which metadata is most relevant to include for different use cases, and where you […]

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Amazon Personalize now available in EU (Frankfurt) Region

Amazon Personalize is a machine learning (ML) service that enables you to personalize your website, app, ads, emails, and more with private, custom ML models that you can create with no prior ML experience. We’re excited to announce the general availability of Amazon Personalize in the EU (Frankfurt) Region. You can use Amazon Personalize to […]

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Using A/B testing to measure the efficacy of recommendations generated by Amazon Personalize

Machine learning (ML)-based recommender systems aren’t a new concept, but developing such a system can be a resource-intensive task—from data management during training and inference, to managing scalable real-time ML-based API endpoints. Amazon Personalize allows you to easily add sophisticated personalization capabilities to your applications by using the same ML technology used on for […]

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