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NYC Taxi Tutorial

This project demonstrates how to build an ML application and use MLOps to operationalize it.

Notebooks and code

The project contains four notebooks, in the following order:

You can find the python source code under /src

Installation

This project can run in different development environments:

  1. Local computer (using PyCharm, VSCode, Jupyter, etc.)
  2. Inside GitHub Codespaces
  3. Sagemaker studio and Studio Labs (free edition) or other managed Jupyter environments

Install the code and mlrun client

To get started, fork this repo into your GitHub account and clone it into your development environment.

To install the package dependencies (not required in GitHub codespaces) use:

make install-requirements

If you prefer to use Conda or work in Sagemaker use this instead (to create and configure a conda env):

make conda-env

Make sure you open the notebooks and select the mlrun conda environment

Install or connect to MLRun service/cluster

The MLRun service and computation can run locally (minimal setup) or over a remote Kubernetes environment.

If your development environment support docker and have enough CPU resources run:

make mlrun-docker

MLRun UI can be viewed in: http://localhost:8060

If your environment is minimal or you are in Sagemaker run mlrun as a process (no UI):

[conda activate mlrun &&] make mlrun-api

For MLRun to run properly you should set your client environment, this is not required when using codespaces, the mlrun conda environment, or iguazio managed notebooks.

Your environment should include MLRUN_ENV_FILE=<absolute path to the ./mlrun.env file> (point to the mlrun .env file in this repo), see mlrun client setup instructions for details.

Note: You can also use a remote MLRun service (over Kubernetes), instead of starting a local mlrun, edit the mlrun.env and specify its address and credentials

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