Deploy MLflow on Kubernetes: Build Scalable MLOps Pipelines

4.2 ★★★★ ★

Deploy MLflow on Kubernetes: Build Scalable MLOps Pipelines

Name: Master MLflow deployment on Kubernetes to manage and scale your ML
Rating: 4.2 (217 reviews)
Author: Skills Marathon

Master MLflow deployment on Kubernetes to manage, track, and scale your entire machine learning lifecycle.

updated on icon Updated on Jan, 2026

language icon Language - English

person icon Skills Marathon

English [CC]

category icon Development ,CI-CD Pipelines,

Lectures -18

Duration -1.5 hours

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4.2 ★★★★ ★

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Course Description

Learn how to tap into the power of scalable MLOps by using MLflow deployment on Kubernetes. This course will take you through deploying, managing, and scaling a MLflow service in actual machine learning workflows.
You’ll start with a practical introduction to the four key components of MLflow - Tracking, Projects, Models, and Model Registry - then transition to Kubernetes deployment using Helm charts, persistent storage, authentication, and CI/CD pipeline integrations using MinIO, S3, and Azure Blob.
This course is intended for:

Machine Learning Engineers that want to fine-tune and scale their MLOps workflows
DevOps professionals with a role in production ML systems
Anyone building a production-grade MLOps architecture
By the end of this course, you will have the ability to build and manage a production-grade ML platform with Kubeflow that runs in Kubernetes.

Goals

Understand the elements of MLflow and its general architecture
Deploy MLflow Conditionally using Helm on Kubernetes cluster
Enable experiment tracking, model packaging and model registry
Configure persistent storage for model artifacts and model data
Secure MLflow using basic auth and OAuth2 Setup automated
MLflow pipelines to track and automate the model lifecycle
Monitor, scale and troubleshoot MLflow in a production environment
Integrate MLflow with S3, MinIO or Azure Blob
Setup CI/CD pipelines to automate model lifecycle specialists
Monitor, scale and troubleshoot MLflow in a production setting.

Prerequisites

Basic knowledge of Kubernetes and Docker
Familiarity with Python and machine learning workflows
Exposure to Helm and cloud storage (S3, MinIO, Azure Blob)
Willingness to explore MLOps tools and deployment practices

Deploy MLflow on Kubernetes: Build Scalable MLOps Pipelines

Curriculum

Check out the detailed breakdown of what’s inside the course

Introduction to MLflow and Kubernetes

3 Lectures

Understand the course structure, goals, and how MLflow integrates with Kubernetes. 05:38 05:38
Learn about scalability, high availability, resource efficiency, and resilience in ML workflows. 04:54 04:54
Get introduced to Tracking, Projects, Models, and the Model Registry 04:23 04:23

Environment Setup & MLflow Installation

3 Lectures

ML Tracking & Experimentation

3 Lectures

Hyperparameter Tuning & Best Model Selection

3 Lectures

Model Packaging, Testing & Local Deployment

6 Lectures

Instructor Details

Skills Marathon

Skills Marathon is a learning-focused academy dedicated to helping individuals build practical, job-ready skills through clear and engaging training. With an emphasis on hands-on learning and real-world applications, Skills Marathon aims to make complex topics easy to understand and accessible for learners at all levels. Their sessions are designed to boost confidence, encourage continuous improvement, and support learners in achieving their personal and professional goals.

Course Certificate

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Deploy MLflow on Kubernetes: Build Scalable MLOps Pipelines

Deploy MLflow on Kubernetes: Build Scalable MLOps Pipelines