Online Course
Introduction to MLflow

Name: MLflow for Machine Learning | Experiment Tracking, Model Registry & MLOps
Price: 36 USD

This course provides a hands-on introduction to MLflow, a powerful open-source platform for managing the end-to-end machine learning lifecycle. You’ll learn how to track experiments, package and deploy models, and integrate MLflow into production workflows—all without needing advanced MLOps experience.

4.8

862 reviews on

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43 students already enrolled

Skill level:

Advanced

Duration:

1 hour

Lessons (2 hours)

CPE credits:

4

CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of professionals must earn to maintain their licenses and stay current with regulations and best practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's official website: www.nasbaregistry.org

Accredited

certificate

What you learn

Understand the core concepts of MLflow and its role in the ML lifecycle.
Track experiments by logging parameters and metrics using the MLflow Python SDK.
Organize experiments effectively with named experiments, run names, and tags.
Package code into reproducible MLflow Projects with environment definitions.
Log, save, and load models across multiple ML frameworks using built-in flavors.
Serve models locally as REST APIs and prepare for deployment in production.
Use the MLflow Model Registry to version models throughout their lifecycle.
Load models dynamically from the registry using stage-based URIs.
Apply best practices to avoid tracking mistakes and ensure reproducibility.

Topics & tools

MLOps conceptsMLOpsAIMachine and Deep LearningPython

Your instructor

Stefan Micic

MLOps & Data Engineer | Founder at Brainiac | AWS & Azure Certified ML Specialist

Track record

Bringing real-world expertise from leading global companies

Academic background

Master's degree, Artificial Intelligence

MsC in Faculty of Technical Sciences, Novi Sad, Serbia

Ask Stefan a question

Course overview

Accredited certificates

Course OVERVIEW

Description

CPE Credits: 4 Field of Study: Computer Software & App
Delivery Method: QAS Self Study

MLflow is one of the most widely adopted tools for managing machine learning projects, offering experiment tracking, model packaging, deployment, and lifecycle management—all in one platform. This course is designed for data scientists, ML engineers, and developers who want to bring order, structure, and automation into their ML workflows.

Starting from first principles, you’ll learn how to:

Track experiments and compare model performance using the MLflow Tracking API and UI
Package your training code and environments into reproducible MLflow Projects
Save and serve models locally and in the cloud using MLflow Models
Register, version, and promote models using the Model Registry
Integrate MLflow into CI/CD pipelines and extend it with tools like DVC, Optuna, and SageMaker

Through guided lessons, case-based exercises, and practical examples, you’ll move from simple tracking tasks to building a foundation for production-ready machine learning pipelines.

Whether you're managing a personal ML project or scaling a team-wide workflow, this course equips you with the skills and best practices to make MLflow your go-to tool for operationalizing machine learning.

Prerequisites

Basic Understanding of Python
Enthusiasm for Learning

Advanced preparation

None

Curriculum

31 lessons 26 exercises 1 exam

1. Welcome to the 365 Course Builder

3 min

3 min

Hello! Free

Course Introduction: Welcome to MLflow Free
2. MLOps Fundamentals and MLflow Overview

13 min

13 min

Understanding MLOps and Its Importance Free

The Machine Learning Lifecycle and Challenges Free

Introduction to MLflow Free

MLflow Components at a Glance Free

Exercise Free
3. Getting Started with MLflow

12 min

12 min

Installing MLflow and Setting Up the Environment

Running the MLflow Tracking Server (UI)

Exercise

Your First MLflow Experiment (Hello World)

Exercise
4. Experiment Tracking in Depth

16 min

16 min

Logging Parameters, Metrics, and Artifacts

Organizing Experiments and Runs

Exercise

Using MLflow Autologging

Exercise

Viewing and Comparing Results (Advanced)

Exercise
5. MLflow Projects – Packaging for Reproducibility

11 min

11 min

What Are MLflow Projects?

Creating and Running an MLflow Project

Reproducibility and Version Control

Exercise
6. MLflow Models – Packaging and Deploying Models

11 min

11 min

Saving and Loading Models with MLflow

Serving and Deploying MLflow Models

Practical Example – Deploying a Model Locally

Exercise
7. MLflow Model Registry – Managing the Model Lifecycle

20 min

20 min

Introduction to the Model Registry

Registering a Model

Managing Model Versions and Stages

Hands-On: Publishing a Model to the Registry

Exercise
8. Integrating MLflow into Automated MLOps Pipelines

8 min

8 min

MLflow in the Context of a CI/CD Pipeline

Triggering Deployments from the Model Registry

Exercise
9. Best Practices and Tips for MLflow in MLOps

12 min

12 min

Experiment Tracking Best Practices

Model Management Best Practices

Integrating MLflow with Other Tools

Common Pitfalls and How to Avoid Them

Exercise
10. Conclusion and Next Steps

2 min

2 min

Course Wrap-Up: Final Thoughts & Next Steps

Thanks for watching!
11. Course exam

30 min

30 min

Course exam

Free lessons

1.1 Hello!

1 min

1.2 Course Introduction: Welcome to MLflow

2 min

2.1 Understanding MLOps and Its Importance

4 min

2.2 The Machine Learning Lifecycle and Challenges

4 min

2.3 Introduction to MLflow

2 min

2.4 MLflow Components at a Glance

3 min

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