Course Overview
As organisations move generative AI systems into production, ensuring reliability, safety, and performance becomes critical. LLMOps extends traditional MLOps practices to address the unique challenges of large language models, including evaluation complexity, non-deterministic outputs, and trust considerations.
This programme focuses on the operational aspects of generative AI systems, including evaluation frameworks, monitoring pipelines, and trust mechanisms. Participants learn how to measure output quality, track system performance, and implement safeguards that ensure consistent and responsible AI behaviour.
Through practical exercises, learners design evaluation pipelines, implement monitoring strategies, and build trust mechanisms for production-ready generative AI systems.
Hands-On Learning
Participants design evaluation pipelines, monitor LLM outputs, and implement reliability and trust mechanisms in real-world scenarios.
Mentor-Led Sessions
Industry mentors guide learners through LLMOps practices used in production AI systems and enterprise AI platforms.
Career-Ready Skills
Develop critical skills required to operationalise generative AI systems with reliability, observability, and governance.
Learning Outcomes
Understand LLMOps practices for production AI systems
Design evaluation frameworks for generative AI outputs
Monitor LLM performance and system behaviour
Implement trust and safety mechanisms
Detect and manage issues such as hallucinations and drift
Build reliable and production-ready generative AI systems
Prerequisites
Basic programming knowledge (Python or JavaScript helpful)
Familiarity with generative AI or LLM applications
Basic understanding of MLOps concepts helpful
Detailed Syllabus
Organized by professional domains with comprehensive coverage
Topics Covered:
- •What is LLMOps
- •Differences between MLOps and LLMOps
- •Challenges in deploying generative AI systems
- •Production use cases for GenAI
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 LLMOps: Evals, Monitoring & Trust, 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
LLMOps is the practice of managing, monitoring, and improving generative AI systems in production environments.
Ready to Start Your Learning Journey?
Take the next step in your professional development