Deploy MLflow on Kubernetes: Build Scalable MLOps Pipelines
Master MLflow deployment on Kubernetes to manage, track, and scale your entire machine learning lifecycle.
Updated on Jun, 2025
Language - English
Lectures -18
Duration -1.5 hours
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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:
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
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
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