---
url: "https://xcademia.com/courses/mlflow-for-production-ml"
title: MLflow for Production ML
description: "Learn MLflow workflows for experiment tracking, model versioning, and deployment in this mentor-led machine learning course.

"
publishedAt: "2026-03-17T04:55:07.321584+00:00"
updatedAt: "2026-03-30T22:50:53.7265+00:00"
type: course
code: "AID-0027"
level: Practitioner
duration_days: "2"
track: "MLOps & Production AI"
category: "AI, Data & Analytics"
credential_tier: tier1
price_gbp: "1799"
---

# MLflow for Production ML

> Learn how to track experiments, manage models, and deploy ML systems using MLflow workflows. This mentor-led course uses practical scenarios to implement reproducible and production-ready ML pipelines.

## Overview

Managing machine learning experiments and deploying models reliably requires structured workflows and tooling. MLflow has become a widely adopted platform for tracking experiments, packaging models, and managing the ML lifecycle across teams.

This mentor-led programme introduces the core components of MLflow, including experiment tracking, model registry, and deployment workflows. Participants learn how to manage experiments, compare results, version models, and prepare them for production environments.

Through practical scenarios, learners implement MLflow pipelines that support reproducibility, collaboration, and deployment. By the end of the course, participants will understand how to use MLflow to manage the full lifecycle of machine learning systems in real-world environments.

## Prerequisites

- Basic understanding of machine learning concepts
- Familiarity with Python programming
- Experience with basic data workflows

## What you will learn

- Design MLflow-based ML lifecycle workflows
- Analyse experiment tracking strategies
- Implement model packaging and versioning
- Evaluate model performance across experiments
- Communicate ML workflow decisions
- Lead MLflow adoption for production ML

## Skills you will gain

- MLflow experiment tracking
- Model versioning workflows
- ML model packaging
- ML lifecycle management
- Model deployment basics
- Reproducible ML pipelines

## Career progression

- ML Engineer
- MLOps Engineer
- Data Scientist
- AI Engineer
- Data Engineer

## Curriculum

1. **Module 1: Getting Ready**
   - ML lifecycle overview
   - Course tools and environment setup
   - Introduction to MLflow ecosystem
   - Responsible ML practices
2. **Module 2: MLflow Fundamentals**
   - MLflow components overview
   - Experiment tracking concepts
   - Logging parameters and metrics
   - Tracking runs and comparisons
3. **Module 3: Experiment Tracking in Practice**
   - Structuring ML experiments
   - Comparing model performance
   - Managing experiment metadata
   - Reproducibility techniques
4. **Module 4:  Model Packaging and Registry**
   - MLflow model formats
   - Model versioning strategies
   - Model registry workflows
   - Managing model lifecycle stages
5. **Module 5: Model Deployment with MLflow**
   - Deployment options overview
   - Serving models via APIs
   - Integration with applications
   - Deployment best practices
6. **Module 6: Managing Production ML Workflows**
   - Monitoring experiment outcomes
   - Collaboration across teams
   - CI/CD integration basics
   - Scaling MLflow in production

## Exam & certification

You will receive an Xcademia certificate of completion based on participation and successful completion of labs and scenario simulations.

## Delivery options

- **Live Online** — Join live instructor-led sessions from anywhere. Interactive, engaging, and flexible.
- **Onsite Training** — We come to you. Training delivered at your workplace for teams of 6 or more.
- **Venue-Based** — Classroom training at a professional venue. Ideal for focused, immersive learning.
- **Blended** — Combine online and in-person learning for maximum flexibility and impact.

## Frequently asked questions

**What is MLflow used for?**

MLflow is used to track experiments, manage models, and support deployment workflows in machine learning projects.



**Will we use MLflow hands-on?**

Yes. Participants work through practical scenarios using MLflow for experiment tracking and deployment.



**Is this course suitable for data scientists?**

Yes. Data scientists and ML engineers can benefit from structured experiment tracking and model management.



**Does the course cover deployment?**

Yes. It includes model packaging and deployment using MLflow workflows.



**Does this course need an exam?**

No. Completion is based on mentor-led practical scenarios and participation in exercises.

## Course at a glance

| Field | Value |
| --- | --- |
| Code | AID-0027 |
| Duration | 2 days |
| Level | Practitioner |
| Track | MLOps & Production AI |
| Category | AI, Data & Analytics |
| Credential tier | tier1 |
| Price (GBP) | £1799 |

---

## About this content

This Markdown course profile is the citation-grade twin of [MLflow for Production ML](https://xcademia.com/courses/mlflow-for-production-ml). It is published by **Xcademia** (UK Companies House 12322710) and is available for AI search engines and large language models to index, summarise, and cite.

When citing or quoting, please attribute *Xcademia* and link back to the source URL above.

- Source: https://xcademia.com/courses/mlflow-for-production-ml
- Publisher: Xcademia — https://xcademia.com
- Catalogue index: https://xcademia.com/llms-full.txt