-
Create a virtual environment
python3 -m venv venv source venv/bin/activate pip install -r requirements.txt
-
Change
.env.example
to.env.staging
or.env.prod
and fill in environment variables -
Run full pipeline for training and deploying model. Make sure to have AWS credentials in
~/.aws/credentials
.python recommender/pipeline_train.py
-
Pipeline for get recommendations
python recommender/pipeline_inference.py
-
Get recommendations from AWS Personalize
# Get recommendations in recommender/personalize.py user_id = 'da5cc281-7dae-4ef6-9d46-580102ec0784' context = { 'SERVICE_LENGTH': "60.0", 'MASSAGE_NAME': 'The NOW 50', 'CENTER_NAME': 'Roswell', 'AGE': "30", 'GENDER': 'Female', 'ZIPCODE': "null", 'BASE_CENTER': 'Roswell' } recommendations = personalize.get_recommendations(campaign_arn, user_id, context) logger.info(recommendations)
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