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This repository is cloned into the environment so you can explore the more advanced notebooks with this approach as well. -## Amazon Personalize Use Cases examples +## Amazon Personalize Next Steps -The [core_use_cases/](core_use_cases/) folder contains detailed examples of the following typical use cases. +The [next_steps/](next_steps/) folder contains detailed examples of the following typical next steps in your Amazon Personalize journey. This folder contains the following advanced content: -* Collaborative filtering based on user-item interaction tables. The intuition behind is that similar users like similar items. - - [Offline evaluation with 'hrnn' user-based recommendation.](core_use_cases/personalize_temporal_holdout.ipynb) - - [Example of 'sims' item-based recommendation.](core_use_cases/personalize_sims_smell_tests.ipynb) - - [How recommendation changes after 'put_events'.](core_use_cases/personalize_putEvents_demo.ipynb) -* Hybrid recommendation also considering user, item, and event meta-data. The result is to extrapolate to out-of-sample users and items, based on their meta-data features. - - [How to use user, item, and event 'meta-data'.](core_use_cases/personalize_metadata_example.ipynb) - - [Exploring 'cold-start' or 'future' items.](core_use_cases/personalize_coldstart_demo.ipynb) -## Scalable Operations examples for your Amazon Personalize deployments +* Core Use Cases. + - [User Personalization](/next_steps/core_use_cases/user_personalization) + - [Personalize Ranking](/next_steps/core_use_cases/personalize_ranking) + - [Batch Recommendations](/next_steps/core_use_cases/batch_recommendations) + - [Related Items](/next_steps/core_use_cases/related_items) -The [operations/](operations/) folder contains examples on the following topics: +* Scalable Operations examples for your Amazon Personalize deployments + - [MLOps](/next_steps/operations/ml_ops) + - This is a project to showcase how to quickly deploy a Personalize Campaign in a fully automated fashion using AWS Step Functions. To get started navigate to the [ml_ops](operations/ml_ops/) folder and follow the README instructions. + - [Lambda Examples](/next_steps/operations/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](operations/lambda_examples/) folder and follow the README instructions. -* MLOps - - This is a project to showcase how to quickly deploy a Personalize Campaign in a fully automated fashion using AWS Step Functions. To get started navigate to the [ml_ops](operations/ml_ops/) folder and follow the README instructions. -* 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](operations/lambda_examples/) folder and follow the README instructions. -## Reference Architectures +* Workshops + - [Workshops/](/next_steps/workshops/) folder contains a list of our most current workshops: + - POC in a Box + - Re:invent 2019 + - Immersion Days -The [reference_architectures/](reference_architectures/) folder contains reference architectures for the following industries: - -* Retail -* Media and Entertainment -* Travel and Hospitality - -## Workshops - -The [workshops/](workshops/) folder contains a list of our most current workshops: - -* POC in a Box -* Re:invent 2019 - -## Data Science Tools - -The [data_science/](data_science/) folder contains an example on how to approach visualization of the key properties of your input datasets. - -The key components we look out for include: -- Missing data, duplicated events, and repeated item consumptions -- Power-law distribution of categorical fields -- Temporal drift analysis for cold-start applicability -- Analysis on user-session distribution +* Data Science Tools + - The [data_science/](/next_steps/data_science/) folder contains an example on how to approach visualization of the key properties of your input datasets. + - Missing data, duplicated events, and repeated item consumptions + - Power-law distribution of categorical fields + - Temporal drift analysis for cold-start applicability + - Analysis on user-session distribution ## License Summary diff --git a/next_steps/README.md b/next_steps/README.md new file mode 100644 index 0000000..ecf1e33 --- /dev/null +++ b/next_steps/README.md @@ -0,0 +1,53 @@ +# Amazon Personalize Next Steps + +Notebooks and examples on how to onboard and use various features of Amazon Personalize + +## Amazon Personalize Use Cases examples + +The [core_use_cases/](core_use_cases/) folder contains detailed examples of the following typical use cases. + +* Collaborative filtering based on user-item interaction tables. The intuition behind is that similar users like similar items. + - [Offline evaluation with 'hrnn' user-based recommendation.](core_use_cases/personalize_temporal_holdout.ipynb) + - [Example of 'sims' item-based recommendation.](core_use_cases/personalize_sims_smell_tests.ipynb) + - [How recommendation changes after 'put_events'.](core_use_cases/personalize_putEvents_demo.ipynb) +* Hybrid recommendation also considering user, item, and event meta-data. The result is to extrapolate to out-of-sample users and items, based on their meta-data features. + - [How to use user, item, and event 'meta-data'.](core_use_cases/personalize_metadata_example.ipynb) + - [Exploring 'cold-start' or 'future' items.](core_use_cases/personalize_coldstart_demo.ipynb) + +## Scalable Operations examples for your Amazon Personalize deployments + +The [operations/](operations/) folder contains examples on the following topics: + +* MLOps + - This is a project to showcase how to quickly deploy a Personalize Campaign in a fully automated fashion using AWS Step Functions. To get started navigate to the [ml_ops](operations/ml_ops/) folder and follow the README instructions. +* 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](operations/lambda_examples/) folder and follow the README instructions. + +## Reference Architectures + +The [reference_architectures/](reference_architectures/) folder contains reference architectures for the following industries: + +* Retail +* Media and Entertainment +* Travel and Hospitality + +## Workshops + +The [workshops/](workshops/) folder contains a list of our most current workshops: + +* POC in a Box +* Re:invent 2019 + +## Data Science Tools + +The [data_science/](data_science/) folder contains an example on how to approach visualization of the key properties of your input datasets. + +The key components we look out for include: +- Missing data, duplicated events, and repeated item consumptions +- Power-law distribution of categorical fields +- Temporal drift analysis for cold-start applicability +- Analysis on user-session distribution + +## License Summary + +This sample code is made available under a modified MIT license. See the LICENSE file. diff --git a/core_use_cases/README.md b/next_steps/core_use_cases/README.md similarity index 100% rename from core_use_cases/README.md rename to next_steps/core_use_cases/README.md diff --git a/core_use_cases/batch_recommendations/README.md b/next_steps/core_use_cases/batch_recommendations/README.md similarity index 100% rename from core_use_cases/batch_recommendations/README.md rename to next_steps/core_use_cases/batch_recommendations/README.md diff --git a/core_use_cases/batch_recommendations/hrnn_batch_recommendations_example.ipynb b/next_steps/core_use_cases/batch_recommendations/hrnn_batch_recommendations_example.ipynb similarity index 100% rename from core_use_cases/batch_recommendations/hrnn_batch_recommendations_example.ipynb rename to next_steps/core_use_cases/batch_recommendations/hrnn_batch_recommendations_example.ipynb diff --git a/core_use_cases/personalized_ranking/README.md b/next_steps/core_use_cases/personalized_ranking/README.md similarity index 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The content below is designed to help you implement best practices across you Amazon Personalize deployments. - -1. Python Based References -2. Java Based References -3. JavaScript References \ No newline at end of file