---
url: "https://xcademia.com/courses/mlops-engineer"
title: MLOps Engineer
description: "Learn MLOps practices including model deployment, monitoring, drift detection, and reliable ML system management.

"
publishedAt: "2026-03-17T04:26:17.217129+00:00"
updatedAt: "2026-03-30T22:50:53.7265+00:00"
type: course
code: "AID-0025"
level: Professional
duration_days: "5"
track: "MLOps & Production AI"
category: "AI, Data & Analytics"
credential_tier: tier1
price_gbp: "2499"
---

# MLOps Engineer

> Advanced training on operationalising machine learning models with reliable deployment and monitoring practices. Learn MLOps workflows including model packaging, drift detection, and production-ready AI systems.

## 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.

## Prerequisites

- Strong programming knowledge (Python recommended)
- Familiarity with machine learning concepts
- Basic understanding of cloud platforms or DevOps practices helpful

## What you will learn

- 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

## Skills you will gain

- MLOps pipeline design
- Model packaging and deployment
- CI/CD for machine learning
- Monitoring and observability for ML systems
- Drift detection and model lifecycle management
- Incident response and reliability engineering

## Career progression

- MLOps Engineer
- Machine Learning Engineer
- AI Platform Engineer
- DevOps Engineer (AI/ML Systems)
- Data Engineer

## Curriculum

1. **Module 1**
2. **Module 2**
3. **Module 3**
4. **Module 4**

## 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

**1. What is MLOps?**

MLOps is the practice of managing the lifecycle of machine learning models including deployment, monitoring, and maintenance.



**2. Is this course hands-on?**

Yes. Participants build and deploy ML pipelines during practical exercises.



**3. What tools are used in MLOps?**

Tools include CI/CD systems, container platforms, monitoring tools, and ML pipeline orchestration frameworks.



**4. Do I need DevOps experience?**

Basic familiarity is helpful but not mandatory.



**5. What roles benefit most from this training?**

MLOps engineers, ML engineers, AI platform engineers, and DevOps engineers working with AI systems.

## Course at a glance

| Field | Value |
| --- | --- |
| Code | AID-0025 |
| Duration | 5 days |
| Level | Professional |
| Track | MLOps & Production AI |
| Category | AI, Data & Analytics |
| Credential tier | tier1 |
| Price (GBP) | £2499 |

---

## About this content

This Markdown course profile is the citation-grade twin of [MLOps Engineer](https://xcademia.com/courses/mlops-engineer). 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/mlops-engineer
- Publisher: Xcademia — https://xcademia.com
- Catalogue index: https://xcademia.com/llms-full.txt