General Framework in Deploying ML model as a REST API Service

• docker : installation can be found here
• docker-compose : installation can be found here

make docker-compose

GET requests will be available at [http://localhost:1234] with the following input:

- class: [1, 2, 3]
- sex: [M, F]
- age: int
- embarked: ['C', 'S', 'Q']

The list of problems that I would like to tackle in this projects are

• Reproducibility in the whole pipeline

  The projects should be able to be deployed in different environments without hassle. The whole ML pipeline should be agnostic

• Support Continuous Model Training

  The project should be able to build new model automatically whenever there is a change in the dataset. This trained model should be able to be stored and retrieved easily, to be used by the API to perfrom prediction as a service

• Database Update / Data Integrity

  The project should have a proper database suppport to make sure that the model training will always use the latest data.

• Continuous Development / Building

  The model should be able to trained daily - Supports continous development

• Microservice Framework

  Each components will be decoupled - an update to each microservice should not affect the other services.

• Automated Schema Checking / Data Formatting

  The data will undergo an automated Schema Checking which will be used to make sure that the data is ready for training.

The technology stack that we will use in this projects are:

• Scikit-Learn, pandas, numpy : Model training
• Airflow : Model building, Workflow Pipeline
• Postgres DB : Data Storage, Model Storage
• Flask : Restful API
• Docker : Containerization, Decoupling different services

• Postgres DB : Data Storage

• minio : Model and Schema Storage

• Flask : Perform Prediction as a Service to the end user perform links to db container, should be able to read, write model to db, should be able to read data from db

• Pandas, Numpy, Scikit-learn : Model Training
• Airflow : Perform continuous building and deployment

• Airflow : Perform continuous data update

Coming Soon