* Created two services: recommendation training and recommendation serving
* Deleted Docker temporary
* Added features based in signals information
* Added method to get sessions features using PG
* Added same utils and core elements into ml_trainer
* Added checks before training models, added handler for model serving
* Updated serving API and recommendation functions to use frontend signals features
* reorganized modules to have base image and for both serving and training
* Added Dockerfiles and base Dockerfile
* Solved issue while ordering sessions by relevance
* Added method to save user feedback of recommendations
* Added security authorization
* Updated Dockerfile
* fixed issues with secret insertion to API
* Updated feedback structure
* Added git for dags
* Solved issue of insertion on recommendation feedback
* Changed update method from def to async def and it is called during startup
* Solved issues of airflow running mlflow in dag
* Changes sanity checks and added middleware params
* base path renaming
* Changed update method to a interval method which loads one model each 10s if there are models to download
* Added sql files for recommendation service and trainer
* Cleaned files and added documentation for methods and classes
* Added README file
* Renamed endpoints, changed None into empty array and updated readme
* refactor(recommendation): optimized query
* style(recommendation): changed import to top file, renamed endpoints parameters, function optimization
* refactor(recommendation): .gitignore
* refactor(recommendation): .gitignore
* refactor(recommendation): Optimized Dockerfiles
* refactor(recommendation): changed imports
* refactor(recommendation): optimized requests
* refactor(recommendation): optimized requests
* Fixed boot for fastapi, updated some queries
* Fixed issues while downloading models and while returning json response from API
* limited number of recommendations and set a minimum score to present recommendations
* fix(recommendation): fixed some queries and updated prediction method
* Added env value to control number of predictions to make
* docs(recommendation): Added third party libraries used in recommendation service
* frozen requirements
* Update base_crons.py
added `misfire_grace_time` to recommendation crons
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Co-authored-by: Taha Yassine Kraiem <tahayk2@gmail.com>
