MLflow for Kubernetes: The Complete MLOps Guide
Learn how to deploy and manage ML models at scale using MLflow, Kubernetes, KServe, and Docker. A comprehensive guide to production MLOps.
Why MLflow + Kubernetes?
Getting a machine learning model to work in a Jupyter notebook is one thing. Getting it to run reliably in production, at scale, with monitoring and versioning — that's an entirely different challenge.
MLflow handles the ML lifecycle: experiment tracking, model packaging, and registry. Kubernetes handles the infrastructure: scaling, orchestration, and reliability. Together, they form the backbone of modern MLOps.
The MLOps Stack
Here's the stack we'll work with:
- MLflow — experiment tracking, model registry, model packaging
- Kubernetes — container orchestration and scaling
- KServe — model serving on Kubernetes
- Docker — containerization of ML models
- MLServer — local model serving for testing
From Notebook to Production
The typical ML journey looks like this:
- Experiment — train models, tune hyperparameters, track results in MLflow
- Package — wrap the best model in a Docker image
- Test — serve locally with MLServer to validate
- Deploy — push to Kubernetes via KServe
- Monitor — track performance, logs, and service health
What You'll Learn
In our MLflow for Kubernetes course, you'll build this pipeline hands-on:
- Set up MLflow with MLServer support
- Create a local Kubernetes cluster with Kind
- Install KServe for model serving
- Train and track a Wine Quality model
- Perform hyperparameter tuning with RandomizedSearchCV
- Build Docker images from MLflow models
- Deploy to Kubernetes with KServe InferenceService
- Monitor service health and perform inference
Who Is This For?
- ML Engineers moving models from notebooks to production
- Data Scientists who want to understand deployment
- MLOps professionals building scalable pipelines
- DevOps engineers adding ML serving to their stack
Prerequisites
- Basic Python and ML knowledge
- Familiarity with Docker and Kubernetes concepts
- A machine that can run Kind (local Kubernetes)
Get Started
Ready to bridge the gap between experiments and production? Check out our MLflow for Kubernetes course for hands-on, step-by-step training.
Ready to Learn by Doing?
Go beyond blog posts with hands-on video courses. Build real projects with Docker, Ansible, Node.js, and more.