MLOps on Kubernetes: Managing the Model Lifecycle with MLflow and Kubeflow

The Gap Between a Notebook and a Production Model

A data scientist can train a good model in a notebook in an afternoon. Getting that same model into production — with a reproducible training pipeline, a versioned artifact, a documented set of hyperparameters, and a rollback path if it underperforms — is a different problem entirely. Without dedicated tooling, teams end up with model files named final_v3_actually_final.pkl and no record of which dataset or configuration produced them. MLOps tooling exists specifically to close this gap.

MLflow: Experiment Tracking and Model Registry

MLflow addresses the most immediate pain point: knowing what was actually run. Every training job logs its parameters, metrics, and output artifacts to a central tracking server, so comparing fifty experiments becomes a filterable table instead of fifty scattered log files. Its model registry adds a second layer on top — versioned, named models with stage transitions (staging, production, archived) that give you an audit trail of exactly which model version is serving traffic and when it was promoted.

Kubeflow: Orchestrating the Full Pipeline

Where MLflow tracks what happened, Kubeflow orchestrates what happens next. Kubeflow Pipelines define multi-step workflows — data validation, feature engineering, training, evaluation — as a directed graph running natively on Kubernetes, with each step as its own container and its own resource requirements. This matters for GPU efficiency specifically: a data preprocessing step doesn't need a GPU, but a training step does, and Kubeflow schedules each step onto the right node pool automatically instead of reserving GPU capacity for the entire pipeline's duration.

Where Ray and ClearML Fit In

For teams running distributed training or hyperparameter sweeps across many GPUs simultaneously, Ray provides the distributed compute layer that MLflow and Kubeflow don't — it handles the actual parallelization of training jobs across a cluster. ClearML is a common lighter-weight alternative to running MLflow and Kubeflow together, bundling experiment tracking, orchestration, and a model registry into a single platform for teams that want less infrastructure to manage at the cost of some flexibility.

Recommended Architecture

• MLflow — experiment tracking and model registry
• Kubeflow — pipeline orchestration across training stages
• Ray — distributed training and hyperparameter tuning
• MinIO — artifact and model storage

Recommended NexNodo Deployment

Training pipelines need real GPU headroom, not just enough for a single job. The recommended deployment is a Managed Kubernetes GPU Medium:

• 2× H200 GPU
• 30 vCPU
• 512 GB RAM
• 2 TB Storage
• $7.90/hr per node or $5,767/mo

Deploy Faster

MLflow, Kubeflow, Ray, and ClearML are all available from the NexNodo AI Catalog, deployable directly onto your Kubernetes cluster alongside the storage and compute you already have provisioned.