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‎README.md‎

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@@ -52,7 +52,233 @@ the AWS Well-Architected Framework. This solution uses the following AWS CDK Sol
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You can launch this solution with one click from [AWS Solutions Implementations](https://aws.amazon.com/solutions/implementations/maintaining-personalized-experiences-with-ml).
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To customize the solution, or to contribute to the solution, follow the steps below:
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To customize the solution, or to contribute to the solution, see [Creating a custom build](#creating-a-custom-build)
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## Configuration
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This solution uses **parameter files**. The parameter file contains all the necessary information to create and maintain
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your resources in Amazon Personalize.
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The file can contain the following sections
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- `datasetGroup`
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- `datasets`
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- `solutions` (can contain `campaigns` and `batchInferenceJobs`)
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- `eventTracker`
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- `filters`
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<details>
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<summary>See a sample of the parameter file</summary>
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```json
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{
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"datasetGroup": {
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"serviceConfig": {
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"name": "dataset-group-name"
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},
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"workflowConfig": {
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"schedules": {
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"import": "cron(0 */6 * * ? *)"
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}
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}
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},
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"datasets": {
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"users": {
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"dataset": {
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"serviceConfig": {
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"name": "users-data"
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}
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},
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"schema": {
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"serviceConfig": {
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"name": "users-schema",
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"schema": {
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"type": "record",
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"name": "users",
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"namespace": "com.amazonaws.personalize.schema",
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"fields": [
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{
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"name": "USER_ID",
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"type": "string"
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},
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{
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"name": "AGE",
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"type": "int"
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},
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{
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"name": "GENDER",
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"type": "string",
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"categorical": true
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}
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]
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}
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}
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}
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},
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"interactions": {
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"dataset": {
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"serviceConfig": {
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"name": "interactions-data"
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}
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},
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"schema": {
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"serviceConfig": {
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"name": "interactions-schema",
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"schema": {
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"type": "record",
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"name": "interactions",
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"namespace": "com.amazonaws.personalize.schema",
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"fields": [
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{
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"name": "ITEM_ID",
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"type": "string"
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},
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{
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"name": "USER_ID",
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"type": "string"
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},
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{
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"name": "TIMESTAMP",
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"type": "long"
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},
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{
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"name": "EVENT_TYPE",
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"type": "string"
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},
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{
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"name": "EVENT_VALUE",
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"type": "float"
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}
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]
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}
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}
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}
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}
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},
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"solutions": [
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{
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"serviceConfig": {
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"name": "sims-solution",
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"recipeArn": "arn:aws:personalize:::recipe/aws-sims"
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},
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"workflowConfig": {
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"schedules": {
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"full": "cron(0 0 ? * 1 *)"
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}
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}
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},
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{
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"serviceConfig": {
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"name": "popularity-count-solution",
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"recipeArn": "arn:aws:personalize:::recipe/aws-popularity-count"
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},
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"workflowConfig": {
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"schedules": {
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"full": "cron(0 1 ? * 1 *)"
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}
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}
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},
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{
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"serviceConfig": {
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"name": "user-personalization-solution",
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"recipeArn": "arn:aws:personalize:::recipe/aws-user-personalization"
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},
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"workflowConfig": {
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"schedules": {
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"full": "cron(0 2 ? * 1 *)"
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}
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},
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"campaigns": [
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{
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"serviceConfig": {
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"name": "user-personalization-campaign",
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"minProvisionedTPS": 1
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}
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}
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],
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"batchInferenceJobs": [
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{
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"serviceConfig": {},
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"workflowConfig": {
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"schedule": "cron(0 3 * * ? *)"
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}
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}
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]
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}
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],
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"eventTracker": {
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"serviceConfig": {
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"name": "dataset-group-name-event-tracker"
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}
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},
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"filters": [
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{
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"serviceConfig": {
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"name": "clicked-or-streamed",
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"filterExpression": "INCLUDE ItemID WHERE Interactions.EVENT_TYPE in (\"click\", \"stream\")"
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}
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},
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{
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"serviceConfig": {
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"name": "interacted",
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"filterExpression": "INCLUDE ItemID WHERE Interactions.EVENT_TYPE in (\"*\")"
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}
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}
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]
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}
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```
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</details>
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This solution allows you to manage multiple dataset groups through the use of multiple parameter files. All .json files
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discovered under the `train/` prefix will trigger the workflow however, the following structure is recommended:
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```
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train/
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│
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├── <dataset_group_1>/ (option 1 - single csv files for data import)
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│ ├── config.json
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│ ├── interactions.csv
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│ ├── items.csv (optional)
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│ └── users.csv (optional)
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│
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└── <dataset_group_2>/ (option 2 - multiple csv files for data import)
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├── config.json
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├── interactions/
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│ ├── <interactions_part_1>.csv
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│ ├── <interactions_part_2>.csv
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│ └── <interactions_part_n>.csv
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├── users/ (optional)
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│ ├── <users_part_1>.csv
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│ ├── <users_part_2>.csv
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│ └── <users_part_n>.csv
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└── items/ (optional)
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├── <items_part_1>.csv
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├── <items_part_2>.csv
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└── <items_part_n>.csv
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```
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If batch inference jobs are required, [batch inference job configuration files](https://docs.aws.amazon.com/personalize/latest/dg/recommendations-batch.html#batch-data-upload)
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must also be uploaded to the following lcoation:
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```
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batch/
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│
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└── <dataset_group_name>/
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└── <solution_name>/
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└── job_config.json
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```
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Batch inference output will be produced at the following location:
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```
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batch/
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│
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└── <dataset_group_name>/
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└── <solution_name>/
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└── <solution_name_YYYY_MM_DD_HH_MM_SS>/
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├── _CHECK
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└── job_config.json.out
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```
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## Creating a custom build
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To customize the solution, follow the steps below:
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S3 bucket where the name is `<DIST_BUCKET_PREFIX>-<REGION_NAME>`. The solution's CloudFormation template will expect the
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source code to be located in the bucket matching that name.
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- `$SOLUTION_NAME` - The name of This solution (example: personalize-solution-customization)
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- `$VERSION` - The version number to use (example: v1.0.0)
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- `$VERSION` - The version number to use (example: v1.0.1)
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- `$REGION_NAME` - The region name to use (example: us-east-1)
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This will result in all global assets being pushed to the `DIST_BUCKET_PREFIX`, and all regional assets being pushed to

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