The Maintaining Personalized Experiences with Machine Learning solution provides a mechanism to automate much of the workflow around Amazon Personalize. This includes dataset group creation, dataset creation and import, solution creation, solution version creation, campaign creation and batch inference job creation

Scheduled rules can be configured for setting up import jobs, solution version retraining (with campaign update) and batch inference job creation.

• Architecture for the AWS MLOps for Amazon Personalize Solution
• AWS CDK Constructs
• Deployment
• Creating a custom build
• Collection of operational metrics

The following describes the architecture of the solution

The AWS CloudFormation template deploys the resources required to automate your Amazon Personalize usage and deployments. The template includes the following components:

1. An Amazon S3 bucket used to store personalization data and configuration files.
2. An AWS Lambda function triggered when new/ updated personalization configuration is uploaded to the personalization data bucket.
3. An AWS Stepfunctions workflow to manage all of the resources of an Amazon Personalize dataset group (including datasets, schemas, event tracker, filters, solutions, campaigns, and batch inference jobs).
4. CloudWatch metrics for Amazon Personalize for each new trained solution version are added to help you evaluate the performance of a model over time.
5. An Amazon Simple Notification Service (SNS) topic and subscription to notify an administrator when the maintenance workflow has completed via email.
6. DynamoDB is used to track the scheduled events configured for Amazon Personalize to fully or partially retrain solutions, (re) import datasets and perform batch inference jobs.
7. An AWS Stepfunctions workflow is used to track the current running scheduled events, and invokes step functions to perform solution maintenance (creating new solution versions, updating campaigns), import updated datasets, and perform batch inference.
8. A set of maintenance AWS Stepfunctions workflows are provided to:
   1. Create new dataset import jobs on schedule
   2. Perform solution FULL retraining on schedule (and update associated campaigns)
   3. Perform solution UPDATE retraining on schedule (and update associated campaigns)
   4. Create batch inference jobs
9. An Amazon EventBridge event bus, where resource status notification updates are posted throughout the AWS Step functions workflow
10. A command line interface (CLI) lets existing resources be imported and allows schedules to be established for resources that already exist in Amazon Personalize

Note: From v1.0.0, AWS CloudFormation template resources are created by the AWS CDK and AWS Solutions Constructs.

AWS CDK Solutions Constructs make it easier to consistently create well-architected applications. All AWS Solutions Constructs are reviewed by AWS and use best practices established by the AWS Well-Architected Framework. This solution uses the following AWS CDK Solutions Constructs:

• aws-lambda-sns

You can launch this solution with one click from AWS Solutions Implementations.

To customize the solution, or to contribute to the solution, see Creating a custom build

This solution uses parameter files. The parameter file contains all the necessary information to create and maintain your resources in Amazon Personalize.

The file can contain the following sections

• datasetGroup
• datasets
• solutions (can contain campaigns and batchInferenceJobs)
• eventTracker
• filters

{
  "datasetGroup": {
    "serviceConfig": {
      "name": "dataset-group-name"
    },
    "workflowConfig": {
      "schedules": {
        "import": "cron(0 */6 * * ? *)"
      }
    }
  },
  "datasets": {
    "users": {
      "dataset": {
        "serviceConfig": {
          "name": "users-data"
        }
      },
      "schema": {
        "serviceConfig": {
          "name": "users-schema",
          "schema": {
            "type": "record",
            "name": "users",
            "namespace": "com.amazonaws.personalize.schema",
            "fields": [
              {
                "name": "USER_ID",
                "type": "string"
              },
              {
                "name": "AGE",
                "type": "int"
              },
              {
                "name": "GENDER",
                "type": "string",
                "categorical": true
              }
            ]
          }
        }
      }
    },
    "interactions": {
      "dataset": {
        "serviceConfig": {
          "name": "interactions-data"
        }
      },
      "schema": {
        "serviceConfig": {
          "name": "interactions-schema",
          "schema": {
            "type": "record",
            "name": "interactions",
            "namespace": "com.amazonaws.personalize.schema",
            "fields": [
              {
                "name": "ITEM_ID",
                "type": "string"
              },
              {
                "name": "USER_ID",
                "type": "string"
              },
              {
                "name": "TIMESTAMP",
                "type": "long"
              },
              {
                "name": "EVENT_TYPE",
                "type": "string"
              },
              {
                "name": "EVENT_VALUE",
                "type": "float"
              }
            ]
          }
        }
      }
    }
  },
  "solutions": [
    {
      "serviceConfig": {
        "name": "sims-solution",
        "recipeArn": "arn:aws:personalize:::recipe/aws-sims"
      },
      "workflowConfig": {
        "schedules": {
          "full": "cron(0 0 ? * 1 *)"
        }
      }
    },
    {
      "serviceConfig": {
        "name": "popularity-count-solution",
        "recipeArn": "arn:aws:personalize:::recipe/aws-popularity-count"
      },
      "workflowConfig": {
        "schedules": {
          "full": "cron(0 1 ? * 1 *)"
        }
      }
    },
    {
      "serviceConfig": {
        "name": "user-personalization-solution",
        "recipeArn": "arn:aws:personalize:::recipe/aws-user-personalization"
      },
      "workflowConfig": {
        "schedules": {
          "full": "cron(0 2 ? * 1 *)"
        }
      },
      "campaigns": [
        {
          "serviceConfig": {
            "name": "user-personalization-campaign",
            "minProvisionedTPS": 1
          }
        }
      ],
      "batchInferenceJobs": [
        {
          "serviceConfig": {},
          "workflowConfig": {
            "schedule": "cron(0 3 * * ? *)"
          }
        }
      ]
    }
  ],
  "eventTracker": {
    "serviceConfig": {
      "name": "dataset-group-name-event-tracker"
    }
  },
  "filters": [
    {
      "serviceConfig": {
        "name": "clicked-or-streamed",
        "filterExpression": "INCLUDE ItemID WHERE Interactions.EVENT_TYPE in (\"click\", \"stream\")"
      }
    },
    {
      "serviceConfig": {
        "name": "interacted",
        "filterExpression": "INCLUDE ItemID WHERE Interactions.EVENT_TYPE in (\"*\")"
      }
    }
  ]
}

This solution allows you to manage multiple dataset groups through the use of multiple parameter files. All .json files discovered under the train/ prefix will trigger the workflow however, the following structure is recommended:

train/
│
├── <dataset_group_1>/ (option 1 - single csv files for data import)
│   ├── config.json
│   ├── interactions.csv
│   ├── items.csv (optional)
│   └── users.csv (optional)
│   
└── <dataset_group_2>/ (option 2 - multiple csv files for data import)
    ├── config.json
    ├── interactions/
    │   ├── <interactions_part_1>.csv
    │   ├── <interactions_part_2>.csv
    │   └── <interactions_part_n>.csv
    ├── users/ (optional)
    │   ├── <users_part_1>.csv
    │   ├── <users_part_2>.csv
    │   └── <users_part_n>.csv
    └── items/ (optional)
        ├── <items_part_1>.csv
        ├── <items_part_2>.csv
        └── <items_part_n>.csv

If batch inference jobs are required, batch inference job configuration files must also be uploaded to the following lcoation:

batch/
│
└── <dataset_group_name>/
    └── <solution_name>/
        └── job_config.json  

Batch inference output will be produced at the following location:

batch/
│
└── <dataset_group_name>/
    └── <solution_name>/
        └── <solution_name_YYYY_MM_DD_HH_MM_SS>/
            ├── _CHECK
            └── job_config.json.out   

To customize the solution, follow the steps below:

The following procedures assumes that all the OS-level configuration has been completed. They are:

• AWS Command Line Interface
• Python 3.9 or newer
• Node.js 16.x or newer
• AWS CDK 2.7.0 or newer
• Amazon Corretto OpenJDK 11

Please ensure you test the templates before updating any production deployments.

git clone https://github.com/aws-solutions/maintaining-personalized-experiences-with-machine-learning

python -m virtualenv .venv 
source ./.venv/bin/activate 
cd ./source 
pip install -r requirements-dev.txt 

pytest --cov 

Packaging and deploying the solution with the AWS CDK allows for the most flexibility in development

cd ./source/infrastructure 

# set environment variables required by the solution
export BUCKET_NAME="my-bucket-name"

# bootstrap CDK (required once - deploys a CDK bootstrap CloudFormation stack for assets)  
cdk bootstrap --cloudformation-execution-policies arn:aws:iam::aws:policy/AdministratorAccess

# build the solution 
cdk synth

# build and deploy the solution 
cdk deploy

It is highly recommended to use the AWS CDK to deploy this solution (using the instructions above). While CDK is used to develop the solution, to package the solution for release as a CloudFormation template, use the build-s3-cdk-dist build tool:

cd ./deployment

export DIST_BUCKET_PREFIX=my-bucket-name  
export SOLUTION_NAME=my-solution-name  
export VERSION=my-version  
export REGION_NAME=my-region

build-s3-cdk-dist deploy \
  --source-bucket-name DIST_BUCKET_PREFIX \
  --solution-name SOLUTION_NAME \
  --version_code VERSION \
  --cdk-app-path ../source/infrastructure/deploy.py \
  --cdk-app-entrypoint deploy:build_app \
  --region REGION_NAME \
  --sync

Parameter Details

• $DIST_BUCKET_PREFIX - The S3 bucket name prefix. A randomized value is recommended. You will need to create an S3 bucket where the name is <DIST_BUCKET_PREFIX>-<REGION_NAME>. The solution's CloudFormation template will expect the source code to be located in the bucket matching that name.
• $SOLUTION_NAME - The name of This solution (example: personalize-solution-customization)
• $VERSION - The version number to use (example: v0.0.1)
• $REGION_NAME - The region name to use (example: us-east-1)

This will result in all global assets being pushed to the DIST_BUCKET_PREFIX, and all regional assets being pushed to DIST_BUCKET_PREFIX-<REGION_NAME>. If your REGION_NAME is us-east-1, and the DIST_BUCKET_PREFIX is my-bucket-name, ensure that both my-bucket-name and my-bucket-name-us-east-1 exist and are owned by you.

After running the command, you can deploy the template:

• Get the link of the SOLUTION_NAME.template uploaded to your Amazon S3 bucket
• Deploy the solution to your account by launching a new AWS CloudFormation stack using the link of the template above.

Note: build-s3-cdk-dist will use your current configured AWS_REGION and AWS_PROFILE. To set your defaults, install the AWS Command Line Interface and run aws configure.

This solution collects anonymous operational metrics to help AWS improve the quality of features of the solution. For more information, including how to disable this capability, please see the implementation guide.

Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.