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
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Browse Courses →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
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Subscribe Free →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.
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Ready to go deeper? Check out our hands-on course: MLflow for Kubernetes — practical exercises you can follow along on your own machine.
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