Skip to content

This notebook sets up MLFlow for experiment tracking, including data preprocessing, training a random forest regressor, logging metrics and parameters, hyperparameter optimization with hyperopt, and model registration. It provides steps for environment setup and installation.

Notifications You must be signed in to change notification settings

Mannerow/mlops-homework-02

Repository files navigation

DataTalk's MLOps Zoomcamp - Module 2 Homework

Dataset: https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page

Description:

'homework-02.ipynb' is a notebook that walks through the installation and env setup for MLFlow and then answers a series of questions. The notebook utilizes Python scripts for data preprocessing, training a random forest regressor and logging the metrics and best params using MLFlow, hyperparameter optimization (using hyperopt), and model registering.

Installation:

Create Conda Env:

conda create -n exp-tracking-env python=3.9

Command to Activate Conda Env:

conda activate exp-tracking-env

Installation

pip install -r requirements.txt

About

This notebook sets up MLFlow for experiment tracking, including data preprocessing, training a random forest regressor, logging metrics and parameters, hyperparameter optimization with hyperopt, and model registration. It provides steps for environment setup and installation.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published