# What is Amazon Personalize? Amazon Personalize is a fully managed machine learning service that uses your data to generate item recommendations for your users. It can also generate user segments based on the users' affinity for certain items or item metadata. Common use case include the following: + **Personalizing a video streaming app** – You can use preconfigured or customizable Amazon Personalize resources to add multiple types of personalized video recommendations to your streaming app. For example, *Top picks for you*, *More like X* and *Most popular* video recommendations. + **Adding product recommendations to an ecommerce app** – You can use preconfigured or customizable Amazon Personalize resources to add multiple types of personalized product recommendations to your retail app. For example, *Recommended for you*, *Frequently bought together* and *Customers who viewed X also viewed* product recommendations. + **Adding real-time next best action recommendations to your app** – You can use customizable Amazon Personalize resources to recommend the actions that your users will most likely take based on their behavior. For example, you can add real-time recommendations for enrolling in your loyalty program, downloading your mobile app, or signing up for promotional emails. + **Creating personalized emails** – You can use customizable Amazon Personalize resources to generate batch recommendations for all users on an email list. Then you can use an [AWS service](#related-services) or [third party service](#third-parties) to send users personalized emails recommending items in your catalog. + **Creating a targeted marketing campaign** – You can use Amazon Personalize to generate segments of users who will most likely interact with items in your catalog. Then you can use an [AWS service](#related-services) or [third party service](#third-parties) to create a targeted marketing campaign that promotes different items to different user segments. + **Personalizing search results** – You can use customizable Amazon Personalize resources to personalize search results for your users. For example, Amazon Personalize can re-rank search results that you generate with [OpenSearch](personalize-opensearch.md). For most use cases, Amazon Personalize generates recommendations primarily based on item interaction data. Item interaction data comes from your users interacting with items in your catalog. For example, users clicking different items. Your item interaction data can come from both your historical bulk interaction records in a CSV file, and real-time events from your users as they interact with your catalog. In some cases, Amazon Personalize also uses data from items and users such as genre, price, or gender. And for next best action scenarios, it uses actions and action interaction data. When you import bulk data, you can use Amazon SageMaker AI Data Wrangler to import data from 40\$1 sources and prepare it for Amazon Personalize. For more information, see [Preparing and importing bulk data using Amazon SageMaker AI Data Wrangler](preparing-importing-with-data-wrangler.md). Amazon Personalize includes API operations for real-time personalization, and batch operations for bulk recommendations and user segments. You can get started quickly with use-case optimized recommenders for your business domain, or you can create your own configurable custom resources. **Topics** + [Pricing for Amazon Personalize](#whatis-pricing) + [Guidance for first-time Amazon Personalize users](first-time-user.md) + [Related AWS services and solutions](#related-services) + [Third-party services](#third-parties) + [Learn more](#experienced-user) + [Using Amazon Personalize with an AWS SDK](sdk-general-information-section.md) ## Pricing for Amazon Personalize With Amazon Personalize, there are no minimum fees and no upfront commitments. The [AWS Free Tier](https://aws.amazon.com/free/) provides a monthly quota of up to 20 GB of data processing per available AWS region, up to 100 hours of training time per eligible AWS region, and up to 180,000 recommendation requests. The free tier is valid for the first two months of usage. For a complete list of charges and prices, see [Amazon Personalize pricing](https://aws.amazon.com/personalize/pricing/). # Guidance for first-time Amazon Personalize users If you're a first-time user of Amazon Personalize, the following resources can help you get started. **Topics** + [Discovering Amazon Personalize with the Magic Movie Machine](#explore-with-magic-movie-machine) + [Navigating getting started materials in this guide](#reading-this-guide) ## Discovering Amazon Personalize with the Magic Movie Machine The Magic Movie Machine is an interactive learning experience. It helps you discover Amazon Personalize features and learn more about generating recommendations. For a short introduction, see the video below. Then try the [Magic Movie Machine](https://dohy8sp8i3s5p.cloudfront.net/). [![AWS Videos](http://img.youtube.com/vi/https://www.youtube.com/embed/T87I563uEjo/0.jpg)](http://www.youtube.com/watch?v=https://www.youtube.com/embed/T87I563uEjo) ## Navigating getting started materials in this guide Review the following sections to get started with Amazon Personalize. For a checklist that provides lists of Amazon Personalize features, requirements, and data guidance, see [Readiness checklist](readiness-checklist.md). 1. **[How Amazon Personalize works](how-it-works.md)** – This section introduces the Amazon Personalize workflow and walks you through the steps to create personalized experiences for your users. This section also includes common Amazon Personalize terms and their definitions. Start with this section to make sure you have good understanding of Amazon Personalize workflows and terms before you start getting recommendations. 1. **[Setting up Amazon Personalize](setup.md)** – In this section you set up your AWS account, set up the required permissions to use Amazon Personalize, and set up the AWS CLI and the AWS SDKs to use and manage Amazon Personalize. 1. **[Getting started tutorials](getting-started.md)** – In this section you get started using Amazon Personalize with a simple movie dataset. Complete these tutorials to get hands-on experience with Amazon Personalize. You can choose to either get started with a Domain dataset group or a Custom dataset group: + To get started creating a Domain dataset group, complete the [Getting started prerequisites](gs-prerequisites.md) and then start the tutorials in [Getting started with a Domain dataset group](getting-started-domain.md). + To get started with a Custom dataset group, complete the [Getting started prerequisites](gs-prerequisites.md) and then start the tutorials in [Getting started with a Domain dataset group](getting-started-domain.md). After you complete the getting started exercise, you can start completing the Amazon Personalize workflow. For steps and links to documentation, see [Amazon Personalize workflow](personalize-workflow.md). ## Related AWS services and solutions Amazon Personalize integrates seamlessly with other AWS services and solutions. For example, you can: + Use Amazon SageMaker AI Data Wrangler (Data Wrangler) to import data from 40\$1 sources into an Amazon Personalize dataset. Data Wrangler is a feature of Amazon SageMaker AI Studio that provides an end-to-end solution to import, prepare, transform, and analyze data. For more information, see [Preparing and importing bulk data using Amazon SageMaker AI Data Wrangler](preparing-importing-with-data-wrangler.md). + Use AWS Amplify to record item interaction events. Amplify includes a JavaScript library for recording events from web client applications. And it includes a library for recording events in server code. For more information, see [Amplify Documentation](https://docs.amplify.aws/). + Automate and schedule Amazon Personalize tasks with [Maintaining Personalized Experiences with Machine Learning](https://aws.amazon.com/solutions/implementations/maintaining-personalized-experiences-with-ml/). This AWS Solutions Implementation automates the Amazon Personalize workflow, including data import, solution version training, and batch workflows. ## Third-party services Amazon Personalize works well with various third-party services. + **Amplitude** – You can use Amplitude to track user actions to help you understand your users' behavior. For information on using Amplitude and Amazon Personalize, see the following AWS Partner Network (APN) blog post: [Measuring the Effectiveness of Personalization with Amplitude and Amazon Personalize](https://aws.amazon.com/blogs/apn/measuring-the-effectiveness-of-personalization-with-amplitude-and-amazon-personalize/). + **Braze** – You can use Braze to send users personalized emails recommending items in your catalog. Braze is a market leading messaging platform (email, push, SMS). For a workshop that shows how to integrate Amazon Personalize and Braze, see [Amazon Personalize workshop](https://www.braze.com/docs/partners/message_personalization/dynamic_content/personalized_recommendations/amazon_personalize/workshop). + **Optimizely** – You can use Optimizely to perform A/B testing with Amazon Personalize recommendations. + **Segment** – You can use Segment to send your data to Amazon Personalize. For more information on integrating Segment with Amazon Personalize, see [Amazon Personalize Destination](https://segment.com/docs/connections/destinations/catalog/amazon-personalize/). For a complete list of partners, see [Amazon Personalize Partners](https://aws.amazon.com/personalize/partners/). ## Learn more The following resources provide additional information about Amazon Personalize: + For a quick reference to help you determine if Amazon Personalize fits your use case, see the [Amazon Personalize Cheat Sheet](https://github.com/aws-samples/amazon-personalize-samples/blob/master/PersonalizeCheatSheet2.0.md) in the [Amazon Personalize samples](https://github.com/aws-samples/amazon-personalize-samples) repository. + For a series of videos on how to use Amazon Personalize, see the [Amazon Personalize Deep Dive Video Series](https://www.youtube.com/watch?v=3gJmhoLaLIo) found on YouTube. + For in-depth tutorials and code samples, see the [amazon-personalize-samples GitHub repository](https://github.com/aws-samples/amazon-personalize-samples). # Using Amazon Personalize with an AWS SDK AWS software development kits (SDKs) are available for many popular programming languages. Each SDK provides an API, code examples, and documentation that make it easier for developers to build applications in their preferred language. | SDK documentation | Code examples | | --- | --- | | [AWS SDK for C\$1\$1](https://docs.aws.amazon.com/sdk-for-cpp) | [AWS SDK for C\$1\$1 code examples](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/cpp) | | [AWS CLI](https://docs.aws.amazon.com/cli) | [AWS CLI code examples](https://docs.aws.amazon.com/code-library/latest/ug/cli_2_code_examples.html) | | [AWS SDK for Go](https://docs.aws.amazon.com/sdk-for-go) | [AWS SDK for Go code examples](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/gov2) | | [AWS SDK for Java](https://docs.aws.amazon.com/sdk-for-java) | [AWS SDK for Java code examples](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2) | | [AWS SDK for JavaScript](https://docs.aws.amazon.com/sdk-for-javascript) | [AWS SDK for JavaScript code examples](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3) | | [AWS SDK for Kotlin](https://docs.aws.amazon.com/sdk-for-kotlin) | [AWS SDK for Kotlin code examples](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin) | | [AWS SDK for .NET](https://docs.aws.amazon.com/sdk-for-net) | [AWS SDK for .NET code examples](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3) | | [AWS SDK for PHP](https://docs.aws.amazon.com/sdk-for-php) | [AWS SDK for PHP code examples](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/php) | | [AWS Tools for PowerShell](https://docs.aws.amazon.com/powershell) | [AWS Tools for PowerShell code examples](https://docs.aws.amazon.com/code-library/latest/ug/powershell_5_code_examples.html) | | [AWS SDK for Python (Boto3)](https://docs.aws.amazon.com/pythonsdk) | [AWS SDK for Python (Boto3) code examples](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python) | | [AWS SDK for Ruby](https://docs.aws.amazon.com/sdk-for-ruby) | [AWS SDK for Ruby code examples](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/ruby) | | [AWS SDK for Rust](https://docs.aws.amazon.com/sdk-for-rust) | [AWS SDK for Rust code examples](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/rustv1) | | [AWS SDK for SAP ABAP](https://docs.aws.amazon.com/sdk-for-sapabap) | [AWS SDK for SAP ABAP code examples](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap) | | [AWS SDK for Swift](https://docs.aws.amazon.com/sdk-for-swift) | [AWS SDK for Swift code examples](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/swift) | **Example availability** Can't find what you need? 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