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An ML Pipeline for Short-Term Rental Prices in NYC using Mlflow as orchestration tool and Weights&Biases as artefact storage tool.

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Build an ML Pipeline for Short-Term Rental Prices in NYC

Overview

You are working for a property management company renting rooms and properties for short periods of time on various rental platforms. You need to estimate the typical price for a given property based on the price of similar properties. Your company receives new data in bulk every week. The model needs to be retrained with the same cadence, necessitating an end-to-end pipeline that can be reused.

In this project, such a pipeline is built.


GitHub Project repo Release v1.0.3


Train the model on a new data sample

Let's now test that we can run the release using mlflow without any other pre-requisite. We will train the model on a new sample of data that our company received (sample2.csv):

(be ready for a surprise, keep reading even if the command fails)

> mlflow run https://github.com/VineetKT/nd0821-c2-build-model-workflow-starter.git \
             -v 1.0.3 \
             -P hydra_options="etl.sample='sample2.csv'"

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An ML Pipeline for Short-Term Rental Prices in NYC using Mlflow as orchestration tool and Weights&Biases as artefact storage tool.

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  • Python 64.8%
  • Jupyter Notebook 35.2%