MLflow: The Complete Guide| K21Academy

Developing machine learning (ML) models can be intricate, with numerous stages ranging from data preprocessing to model deployment. Managing these stages and ensuring reproducibility can be difficult. That’s where MLflow comes in—an open-source platform built to simplify the machine learning lifecycle. In this post, we’ll dive into what MLflow is, usage, concepts, and quick mlflow tutorial

What is MLflow?

MLflow is an open-source platform designed to manage the machine learning lifecycle. It provides a set of tools for managing experiments, packaging code into reproducible runs, and deploying models in a way that is consistent, scalable, and easy to monitor.

MLflow was created by Databricks and is designed to simplify the process of managing and tracking machine learning workflows. It allows data scientists to focus on their models while also ensuring that each step—whether it’s training, hyperparameter tuning, or model serving—is well-documented and reproducible.

Related Readings: Mastering Databricks: A Comprehensive Guide to Learning and Interview Preparation

MLflow Core Components

MLflow is composed of four main components that work together to provide a complete solution for managing machine learning workflows:

1. MLflow Tracking

MLflow Tracking is a component that logs and tracks machine learning experiments, including parameters, metrics, and artifacts (like models and datasets). It helps data scientists visualize and compare different runs to identify the best-performing model. The tracking component can be used both locally and remotely via the MLflow server.

2. MLflow Projects

This component helps you package code in a way that makes it easily reproducible. It allows you to organize your machine learning code and dependencies into a standardized format, making it easy to run the code across different environments. MLflow Projects supports the use of Docker and Conda for environment management.

3. MLflow Models

MLflow Models provides a standardized format for packaging and deploying machine learning models. Models can be saved in a format that can be used with different libraries and frameworks (like TensorFlow, Scikit-learn, PyTorch), and MLflow offers multiple ways to serve and deploy those models across platforms.

4. MLflow Registry

The Model Registry is a central hub for managing and storing machine learning models. It helps you track versioning of models, approve models for deployment, and manage stages such as staging, production, or archived. The Model Registry is particularly useful when collaborating across teams and ensuring that models are properly versioned and deployed.

Why Use MLflow?

There are several reasons to adopt MLflow for managing your machine learning projects:

• Reproducibility: MLflow ensures that every experiment, model, and parameter is logged and can be easily reproduced, making it easier to track progress and ensure consistency.
• Collaboration: MLflow allows team members to collaborate more efficiently, as it provides a centralized system to manage experiments, models, and workflows.
• Experiment Tracking: With MLflow Tracking, you can log parameters, metrics, and artifacts, making it simple to compare different versions of models and track their performance over time.
• Model Management: With the Model Registry, versioning and managing models becomes easier. You can track which model is currently in production and which models are still in development.
• Scalability: MLflow supports both local and cloud-based environments, making it easy to scale from small projects to large enterprise-level applications.

Who Uses MLFlow?

MLflow is used by a wide range of companies and individuals who are involved in machine learning and data science. Here are a few examples of users:

• Data Scientists: Those who need to track experiments, manage different versions of models, and ensure reproducibility.
• ML Engineers: Engineers who deploy, monitor, and optimize machine learning models in production environments.
• Data Teams: Teams working in collaboration on shared machine learning workflows, making use of MLflow’s centralized tracking and version control.
• Startups to Enterprises: Whether it’s a small startup or a large enterprise, MLflow’s scalable infrastructure is used by organizations of all sizes to ensure their ML workflows are efficient and reproducible.

Related Readings: MLOps Hands-On Labs & Projects for High-Paying Careers in 2025

Key Concepts in MLflow

When using MLflow, it’s important to understand some key concepts that will help you navigate the platform effectively:

• Run: A single execution of code with a given set of parameters, metrics, and artifacts. Runs are tracked and compared to evaluate model performance.
• Experiment: A group of runs that share the same objective or problem. Experiments are used to track the results of various experiments related to a specific ML problem.
• Artifact: Output generated by a run (e.g., trained models, datasets, or visualizations).
• Tracking URI: The location where MLflow stores all experiment data. This could be local storage or a cloud-based URI.

Advantages of MLflow

Let’s look at what advantages does mlflow provide the users:

• Open Source: MLflow is free to use and open-source, which means you have full access to the code and can contribute or modify it to suit your needs.
• Versatility: Supports multiple ML frameworks like TensorFlow, Scikit-learn, PyTorch, and XGBoost, allowing it to fit into various machine learning workflows.
• Cloud Integration: Easily integrates with cloud platforms like AWS, Azure, and Google Cloud, allowing you to scale your ML operations.
• Ease of Use: MLflow provides simple APIs and an intuitive UI to track experiments, view metrics, and deploy models, making it easy for beginners and experts alike.
• Ecosystem Support: Since it is widely adopted, MLflow integrates with many third-party tools, such as Apache Spark, for distributed training, making it a powerful tool for end-to-end machine learning workflows.

Related Readings: Azure Databricks, Apache spark & Azure Databricks Architecture Overview

Disadvantages of MLflow

1. Limited Model Deployment Options: Although MLflow supports several deployment options, it might not be as feature-rich as specialized deployment platforms like TensorFlow Serving or Seldon.
2. Scalability Issues: While MLflow works well for small to medium-sized ML workflows, large-scale use cases might face scalability issues unless deployed correctly with cloud infrastructure.
3. Requires Some Setup: Although MLflow is easy to use once set up, the initial configuration (e.g., setting up a central server for tracking) can be complex for beginners.
4. Learning Curve for Advanced Features: While basic usage is simple, more advanced features (like model registry or multi-user collaboration) can require a learning curve and deeper understanding of the system.

MLflow vs. Kubeflow

While both MLflow and Kubeflow are platforms aimed at managing machine learning workflows, they have different design philosophies and strengths.

MLflow:

• Primarily focused on managing the ML lifecycle, from experimentation to deployment.
• Offers a simple, lightweight interface to track experiments and models.
• Easier to set up and use, especially for smaller teams or projects.

Kubeflow:

• A more comprehensive, cloud-native ML platform built on Kubernetes for orchestrating complex machine learning pipelines.
• More suited for large-scale, distributed machine learning environments, particularly when working with cloud infrastructure.
• Has a steeper learning curve and is ideal for teams that require advanced deployment features, like continuous training and automated pipeline execution.

Key Differences:

• Deployment: Kubeflow is designed to work in Kubernetes environments, while MLflow can be used in local or cloud setups.
• Complexity: Kubeflow offers a broader suite of tools for automation and scaling, but MLflow is much easier to get started with and might be sufficient for smaller-scale projects.

In summary, MLflow is a great choice for teams looking for a simple, flexible, and powerful tool for tracking and deploying models, while Kubeflow is best for teams looking to automate and scale complex machine learning pipelines at the enterprise level.

MLFlow Tutorial

Using MLflow is extremely easy. It takes only a few lines of code to integrate MLflow logging in your existing code. But first, you have to install MLflow using pip.

Pre-Requisiste:

Before you begin, ensure that you have the following:

• Visual Studio Code installed on your system. If you don’t have it yet, you can download it here.

Step 1: Install MLflow

You can install MLflow via pip:

pip install mlflow

Step 2: Create a Simple Experiment

Create a python script with the following code & run it in the terminal

import numpy as np

from sklearn.linear_model import LogisticRegression

import mlflow

import mlflow.sklearn

if __name__ == "__main__":

    X = np.array([-2, -1, 0, 1, 2, 1]).reshape(-1, 1)

    y = np.array([0, 0, 1, 1, 1, 0])

    lr = LogisticRegression()

    lr.fit(X, y)

    score = lr.score(X, y)

    print("Score: %s" % score)

    mlflow.log_metric("score", score)

    mlflow.sklearn.log_model(lr, "model")

    print("Model saved in run %s" % mlflow.active_run().info.run_uuid)

Step 4: Run the Script

Execute the script in the terminal

python <filename>

It will give the following output

>>> Score: 0.6666666666666666
>>> Model saved in run 9def2bd1c55b4f0985aa5c050b8f71cd

Step 4: View the Results on UI

The post MLflow: The Complete Guide| K21Academy appeared first on Cloud Training Program.