Converting Research Notebook to Operational Pipeline With MLRun

Overview | Running the Demo | Demo Flow | Pipeline Output | Notebooks and Code


This demo demonstrates how to convert existing machine-learning (ML) code to an MLRun project. The demo implements an MLRun project for taxi ride-fare prediction based on a Kaggle notebook with an ML Python script that uses data from the New York City Taxi Fare Prediction competition.

Running the Demo

To run the demo, simply open the mlrun-code.ipynb notebook from an environment with a running MLRun service and run the code cells.

Demo Flow

The code includes the following components:

  1. Data ingestion — ingest NYC taxi-rides data.

  2. Data cleaning and preparation — process the data to prepare it for the model training.

  3. Model training — train an ML model that predicts taxi-ride fares.

  4. Model serving — deploy a function for serving the trained model.

Pipeline Output

converting-to-mlrun pipeline output