intermediate
MLflow for Kubernetes: Deploy and Manage ML Models at Scale
Build scalable MLOps with MLflow, KServe, Docker, and Kubernetes. Automate deployments, monitor models, and workflows. Learn to deploy ML models to Kubernetes at scale, implement CI/CD pipelines, track experiments, perform hyperparameter tuning, and build production-ready ML services.
15 lessons1h 28m
What you'll learn
- Deploy ML models to Kubernetes at scale using MLflow and KServe
- Implement CI/CD pipelines and automate model updates using Kubernetes
- Track experiments, perform hyperparameter tuning, and compare model versions with MLflow
- Build, package, and monitor production-ready ML services with Docker, MLflow, and Kubernetes
Prerequisites
- • Basic Python and ML knowledge
- • Familiarity with Docker and Kubernetes recommended
Lessons
1
Why Kubernetes + MLflow?
6 min
2
MLflow Architecture & Kubernetes Overview
5 min
3
Install MLflow with MLServer Support
5 min
4
Local Kubernetes Cluster Setup with Kind
4 min
5
Installing KServe on Kubernetes
5 min
6
Installing Dashboard on Kubernetes
10 min
7
Training a Wine Quality Model with MLflow
9 min
8
Hyperparameter Tuning using RandomizedSearchCV
7 min
9
Comparing Results in MLflow UI
6 min
10
Understanding MLflow Model Packaging
7 min
11
Serving Locally with MLServer
10 min
12
Building a Docker Image from MLflow Model
12 min
13
Writing the KServe InferenceService YAML
3 min
14
Deploying to Kubernetes and Checking Service Readiness
3 min
15
Conclusion
3 min