Transportation Hub Pipeline

This project is a proof-of-concept of how to execute a basic machine learning pipeline.

.
├── data/                # Directory where data goes.
├── docs/                
├── models/              # Directory where generated models go.
├── notebooks/           
├── references/          
├── reports/             
├── src/                 # Directory where source code goes.
├── Makefile             # Runner for common operations.
├── README.md
└── requirements.txt

Prerequisites

• Linux or WSL
• Python 3
• make

Setup

$make env

Creates a Python virtual environment and installs the necessary dependencies.

$export PIPE_USER="your_username_here"
$export PIPE_PW="your_password_here"

Configures the username and password used when connecting to Snowflake.

Basic Usage

$make all

Pulls down data from Snowflake and creates a simple machine learning model.

$make api

Deploys the toy model as a local API, which can take inputs and return predictions.

$make test_api

Launch several test API calls, printing inputs and outputs.

Architecture Sketch

Further Reading

• Machine Learning in Production: From Models to Products

• Cookiecutter Data Science Template