MLflow - Part 1 - Fundamentals
Introduction
MLfLow is an open-source platform for managing workflows and artifacts in the entire machine learning lifecycle.
Table of content
Example
In this example a ML project is going to be created using the benefits of MLflow.
Installation of MLflow
Create conda environment
conda create --name mlflow_fundamentals
Actvate environment
conda activate mlflow_fundamentals
Install Mlflow
conda install -c conda-forge mlflow
Example
import mlflow
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_diabetes
from sklearn.ensemble import RandomForestRegressor
mlflow.autolog()
db = load_diabetes()
X_train, X_test, y_train, y_test = train_test_split(db.data, db.target)
# Create and train models.
rf = RandomForestRegressor(n_estimators=100, max_depth=6, max_features=3)
rf.fit(X_train, y_train)
# Use the model to make predictions on the test dataset.
predictions = rf.predict(X_test)
Run MLflow ui
mlflow ui
Check http://localhost:5000 to review the runners.
