• Model training
• Generate predictions
• Deploy the model to AWS using SageMaker

|-- data
    |-- submission.csv
|-- sagemaker
     |-- data
     |-- models
     |-- AWS - SageMaker.ipynb
     |-- featurizer_local.py
     |-- featurizer_remote.py
|-- src
    |-- models
        |-- baseline_model.py: Class for training the binary classification model
        |-- transformers.py: ColumnSelector transformer for preprocessing
|-- api_request_exmpl.json
|-- pickled_model.pickle
|-- Model.ipunb: model evaluation notebook

• python3.8
• aws-cli

1. A verbose explanation of the model training and validation could be found here.
2. CSV file with resulting predictions could be found here.

1. Read and explore dataset (excluded from the current repo due to privacy reasons)
2. Train baseline defualt prediction model
3. Evaluate and сompare different models (Logistic Regression and Tree-based ensemble models) with a set of metrics
4. Estimate feature inportance
5. Generate predictions (/data/submission.csv)
6. Dump the best estimator (/pickled_model.pickle)

1. Define Sagemaker session and role
2. Preprocessing data and train the model
3. Create SageMaker Scikit Estimator
4. Batch transform training data
5. Fit a Tree-based Model with the preprocessed data
6. Serial Inference Pipeline with Scikit preprocessor and classifier
7. Deploy model
8. Make a request to the pipeline endpoint

Besides AWS SageMaker I tested some other AWS deployment options including AWS Serverless Application Model (SAM). It allows deploing ML models with Serverless API (AWS Lambda). I managed to expose API endpoint with helloworld application behind in order to try AWS Lambda.

• ECR: Container & Registy
• AWS Lambda: Serving API
• SAM: Serverless Framework

GET request:

https://x3jp27x3t3.execute-api.eu-central-1.amazonaws.com/test/hello

Expected response:

{
    "message": "hello world"
}