Simplifying machine learning workflow with MLflow

MLflow: Overview

Ever wondered how to communicate the model with others and make different versions of the model at the same place without losing the original model? This happens in data science teams without a centralized platform for ML model management which leads to each person saving their own versions in different locations. This is where tools like ML Flow can help.

MLflow is an open-source, machine-learning lifecycle management tool by Databricks. It helps in simplifying machine learning workflow from data analysis to deployment to experimentation. Since it covers end-to-end ML workflow, it’s for everyone including data engineers, scientists, prompt engineers, data governance officers, etc. By enabling all of them to work together, ML Flow allows smooth collaboration across professionals, easier model packaging deployment, and stress-free model management and reproducibility. 

It comes with four crucial components, tracking (for logging code, parameters, artifacts, and comparing experiments), projects (for packaging the reusable and reproducible models), models (for deploying the models onto diverse environments), and model registry (centralized repository for all model versions).

Complete guide on MLflow

Read the full article here to learn how to install ML flow in your environment and how to use it for experiment tracking.

Simplifying Machine Learning Workflow with MLflow(Part-1)

Read the second part of the article to learn how to use ML Flow for packaging ML code, managing, and deploying it to a cloud environment.

Simplifying Machine Learning Workflow with MLFlow(Part-2)

It also discusses how to effectively use ML flow, what are the best practices to follow, and what are its use cases (tracking experiments, deploying models, monitoring their performance, and so on).

Overall, these articles can help you get started with ML Flow—from importing the package to using them to building a docker image to enhancing reproducibility and collaboration.