---
url: "https://xcademia.com/courses/kubeflow-foundations"
title: Kubeflow Foundations
description: "Learn Kubeflow pipelines, ML workflow orchestration, and model deployment in this mentor-led machine learning engineering course.

"
publishedAt: "2026-03-17T05:16:34.742113+00:00"
updatedAt: "2026-04-02T05:32:38.432982+00:00"
type: course
code: "AID-0029"
level: Practitioner
duration_days: "3"
track: "MLOps & Production AI"
category: "AI, Data & Analytics"
credential_tier: tier1
price_gbp: "1999"
---

# Kubeflow Foundations

> Learn how to build, orchestrate, and manage machine learning workflows using Kubeflow. This mentor-led course uses practical scenarios to implement scalable ML pipelines on Kubernetes.

## Overview

Modern machine learning systems require scalable orchestration, automation, and reproducibility. Kubeflow provides a powerful platform for managing end-to-end ML workflows on Kubernetes, enabling teams to build, deploy, and scale models efficiently.

This mentor-led programme introduces the core components of Kubeflow, including pipelines, experiment tracking, and model serving. Participants learn how to design ML workflows, automate training pipelines, and manage model deployments using cloud-native infrastructure.

Through practical scenarios, learners implement Kubeflow pipelines that integrate data processing, model training, and deployment steps. By the end of the course, participants will understand how to use Kubeflow to operationalise machine learning systems in production environments.

## Prerequisites

- Basic understanding of machine learning concepts
- Familiarity with Python programming
- Basic knowledge of containers or Kubernetes

## What you will learn

- Design Kubeflow-based ML pipelines
- Analyse workflow orchestration strategies
- Implement scalable ML training pipelines
- Evaluate pipeline performance and reliability
- Communicate ML platform architecture decisions
- Lead deployment of ML workflows in production

## Skills you will gain

- Kubeflow pipeline design
- ML workflow orchestration
- Experiment tracking basics
- Model deployment pipelines
- Kubernetes for ML
- ML platform operations

## Career progression

- ML Engineer
- MLOps Engineer
- AI Platform Engineer
- Data Engineer
- Cloud Engineer

## Curriculum

1. **Module 1: Getting Ready**
   - ML platform ecosystem overview
   - Course tools and environment setup
   - Introduction to Kubernetes basics
   - Responsible AI and ML practices
2. **Module 2:  Kubeflow Fundamentals**
   - Kubeflow architecture overview
   - Core components and services
   - Kubeflow use cases
   - Navigating the Kubeflow environment
3. **Module 3: Kubeflow Pipelines**
   - Pipeline concepts and design
   - Building ML pipelines
   - Pipeline components and steps
   - Managing pipeline runs
4. **Module 4: Experimentation and Tracking**
   - Experiment tracking concepts
   - Managing experiments in Kubeflow
   - Comparing model runs
   - Reproducibility techniques
5. **Module 5:  Model Training and Serving**
   - Training workflows in Kubeflow
   - Model serving concepts
   - Deploying models to production
   - API-based inference
6. **Module 6:  Scaling and Production Workflows**
   - Scaling ML pipelines
   - Resource management in Kubernetes
   - CI/CD integration basics
   - Monitoring and optimisation

## Exam & certification

## 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 Kubeflow used for?**

Kubeflow is used to build, orchestrate, and manage machine learning workflows on Kubernetes.



**Will we build a real ML pipeline?**

Yes. Participants create a working Kubeflow pipeline covering training and deployment steps.



**Do I need Kubernetes experience?**

Basic familiarity helps, but key concepts are introduced during the course.



**Is this course suitable for MLOps roles?**

Yes. It is highly relevant for engineers working on ML platforms and automation.



**Does this course need an exam?**

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

## Course at a glance

| Field | Value |
| --- | --- |
| Code | AID-0029 |
| Duration | 3 days |
| Level | Practitioner |
| Track | MLOps & Production AI |
| Category | AI, Data & Analytics |
| Credential tier | tier1 |
| Price (GBP) | £1999 |

---

## About this content

This Markdown course profile is the citation-grade twin of [Kubeflow Foundations](https://xcademia.com/courses/kubeflow-foundations). 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/kubeflow-foundations
- Publisher: Xcademia — https://xcademia.com
- Catalogue index: https://xcademia.com/llms-full.txt