Skip to content

Latest commit

 

History

History
8 lines (7 loc) · 803 Bytes

README.md

File metadata and controls

8 lines (7 loc) · 803 Bytes

Tracking Machine Learning Experiments in Python with MLflow

As the climate changes, predicting the weather becomes ever more important for businesses. Since the weather depends on a lot of different factors, we want to run a lot of experiments to determine what the best approach is to predict the weather.

In this project, we will use London Weather data sourced from Kaggle to try and predict the temperature.
The focus of this code-along is running machine learning experiments. We will first do some minor exploratory data analysis, and then use MLflow to run experiments on what models and what hyperparameters to use.
This is interesting for those that have already trained a machine learning model before and want to see how they can speed up the process of tracking the best model.