Course 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.
Hands-On Learning
Track experiments and deploy a model using MLflow workflows and registry features.
Mentor-Led Sessions
Mentor-led sessions exploring real MLflow pipelines and production use cases.
Career-Ready Skills
Manage ML experiments and model lifecycle using MLflow.
Learning Outcomes
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
Prerequisites
Basic understanding of machine learning concepts
Familiarity with Python programming
Experience with basic data workflows
Detailed Syllabus
Step-by-step learning journey from basics to professional practice
Topics Covered
- ML lifecycle overview
- Course tools and environment setup
- Introduction to MLflow ecosystem
- Responsible ML practices
Skills You'll Gain
Master these in-demand skills through hands-on practice
Career Progression
A clear view of the roles this programme supports, what typically comes next, and where learners progress over time
Ways to Learn
Choose the learning format that works best for you and your team
Live Online
Instructor-Led Training
Join live instructor-led sessions from anywhere. Interactive, engaging, and flexible.
- Live instructor interaction (real-time)
- Trainer-led walkthroughs and real examples
- Guided resources and session notes provided
- Structured Q&A and practical discussion
Price per person
Group enrolments and early planning options available.
All prices are exclusive of VAT where applicable. Group enrolments and custom packages available on request.
Prefer a Faster, Personalised Route into IT?
Not everyone learns best in a group. If you want focused guidance, faster clarity, and confidence you can use on the job, our 1-to-1 Fast-Track Training gives you private, mentor-led support tailored to your experience and goals.
"Many learners choose 1-to-1 when they want understanding, not memorisation."
Exam & Certification Information
Everything you need to know about the certification exams
Important Information
You will receive an Xcademia certificate of completion based on participation and successful completion of labs and scenario simulations.
Credential
Certificate of Completion
On successful completion of MLflow for Production ML, learners receive an Xcademia Certificate of Completion. This standalone certificate is issued directly by Xcademia and is aligned with globally recognised frameworks and best practices.
Frequently Asked Questions
Everything you need to know about this course
MLflow is used to track experiments, manage models, and support deployment workflows in machine learning projects.
Ready to Start Your Learning Journey?
Take the next step in your professional development