Course Overview
Operationalising machine learning models in production requires robust engineering practices that ensure reliability, scalability, and maintainability. This programme prepares engineers to design and implement end-to-end MLOps pipelines for deploying and managing machine learning systems.
Participants learn how to package models, automate deployment pipelines, monitor performance, and detect model drift in real-world environments. The course emphasises building resilient AI systems with strong observability, incident response playbooks, and continuous delivery workflows.
Through hands-on labs, learners implement production-grade MLOps pipelines and gain experience managing the full lifecycle of machine learning models from development to deployment and monitoring.
Hands-On Learning
Participants build an end-to-end MLOps pipeline including model packaging, deployment, monitoring, and drift detection.
Mentor-Led Sessions
Industry mentors guide learners through production MLOps architectures used in enterprise AI platforms.
Career-Ready Skills
Develop advanced skills required to manage and scale machine learning systems in production environments.
Learning Outcomes
Design and implement end-to-end MLOps pipelines
Package and deploy machine learning models into production
Monitor model performance and system health
Detect and handle model and data drift
Implement incident response and recovery strategies
Build reliable and scalable AI deployment systems
Prerequisites
Strong programming knowledge (Python recommended)
Familiarity with machine learning concepts
Basic understanding of cloud platforms or DevOps practices helpful
Detailed Syllabus
Organized by professional domains with comprehensive coverage
Topics Covered:
- •What is MLOps
- •ML lifecycle management
- •Differences between DevOps and MLOps
- •Challenges in deploying ML systems
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 MLOps Engineer, 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
MLOps is the practice of managing the lifecycle of machine learning models including deployment, monitoring, and maintenance.
Ready to Start Your Learning Journey?
Take the next step in your professional development