Machine learning teams often hit the same wall: it’s not the model that’s hard-it’s everything around it. Reproducibility, experiment tracking, consistent deployments, automated retraining, monitoring, and governance quickly become the real bottlenecks.
That’s where MLOps comes in. And two of the most common platforms you’ll hear about are MLflow and Kubeflow. They’re both powerful, widely adopted, and frequently compared-but they solve different parts of the MLOps puzzle.
This guide breaks down what MLflow and Kubeflow are, when to use each, how they complement each other, and how to choose a practical setup for real machine learning production workloads.
What Is MLOps (In Practical Terms)?
MLOps is the set of practices and tooling that helps teams reliably build, deploy, and operate machine learning systems in production.
A practical MLOps workflow typically includes:
- Experiment tracking (metrics, parameters, artifacts, lineage)
- Reproducible training (versioned code + data + environments)
- Model packaging and deployment (consistent handoff to production)
- CI/CD for ML (automated tests, validations, gated releases)
- Orchestration (pipelines, scheduling, dependencies)
- Monitoring (performance, drift, data quality, latency)
- Governance (approvals, auditability, access controls)
MLflow and Kubeflow each cover parts of this-often with overlap, but with different strengths.
MLflow: The Lightweight Workhorse for Experiment Tracking and Model Lifecycle
MLflow is an open-source platform designed to manage the end-to-end machine learning lifecycle-especially around experimentation, reproducibility, and model management.
Key MLflow Components (Quick Overview)
1) MLflow Tracking
Logs and queries:
- Parameters (e.g., learning rate, batch size)
- Metrics (e.g., accuracy, AUC, F1)
- Artifacts (plots, model binaries, confusion matrices)
- Code versions and run metadata
This is often the first MLOps tool teams adopt because it immediately makes work visible and reproducible.
2) MLflow Projects
Standardizes how training runs are executed using:
- A project structure
- Environment management (conda, Docker)
- Entry points for repeatable execution
3) MLflow Models
A packaging format that supports multiple “flavors” (scikit-learn, XGBoost, PyTorch, etc.) and simplifies deployment.
4) MLflow Model Registry
A central place to:
- Version models
- Promote models through stages (e.g., Staging → Production)
- Track lineage and approvals (depending on implementation)
Where MLflow Shines
MLflow is a strong fit when you need:
- Simple, fast experiment tracking
- A model registry for promotion and versioning
- A tool that’s flexible across many environments (local, VM, cloud)
- A low-friction setup for teams not fully on Kubernetes
Common Use Cases for MLflow
- Data science teams iterating quickly on models
- Organizations standardizing training runs and model handoffs
- Teams needing traceability for compliance or audits
- Multi-model environments where consistent versioning matters
Kubeflow: Kubernetes-Native Pipelines and Production-Grade Orchestration
Kubeflow is an open-source platform for machine learning on Kubernetes. Think of it as a toolkit for building production ML systems where automation, scalability, and orchestration are essential.
What Kubeflow Typically Includes
1) Kubeflow Pipelines (KFP)
This is the star of the show for many teams: pipeline orchestration.
You can define and run multi-step workflows such as:
- Data ingestion and validation
- Feature engineering
- Training and hyperparameter tuning
- Evaluation and model approval gates
- Deployment steps
Pipelines are versioned, repeatable, and can be scheduled or triggered.
2) Training Operators (Distributed Training)
Kubeflow supports scalable training patterns (depending on setup), often used for:
- TensorFlow training jobs
- PyTorch distributed training
- MPI / Horovod use cases
3) Model Serving (Often via KServe)
Kubeflow ecosystems commonly use Kubernetes-native serving layers that support:
- Autoscaling
- Canary rollouts
- GPU scheduling (if configured)
- Standard inference endpoints
Where Kubeflow Shines
Kubeflow is typically the right choice when you need:
- Pipeline orchestration across many steps and systems
- Kubernetes-native operations with scalability and isolation
- Repeatable ML workflows for multiple teams
- Integration into platform engineering and DevOps standards
Common Use Cases for Kubeflow
- ML platforms serving multiple product teams
- Regulated environments that require strict automation and audit trails
- Organizations standardizing ML delivery through Kubernetes
- Workloads that benefit from distributed training and scheduling
MLflow vs Kubeflow: What’s the Difference?
Here’s the simplest way to frame it:
- MLflow = Track experiments + manage models
- Kubeflow = Orchestrate pipelines + run ML on Kubernetes
Quick Comparison Table
| Category | MLflow | Kubeflow |
|—|—|—|
| Best for | Experiment tracking, model registry | Pipeline orchestration, Kubernetes-native ML |
| Setup complexity | Low to medium | Medium to high |
| Works without Kubernetes | Yes | Not really (Kubernetes-first) |
| Model registry | Strong built-in | Often external or integrated via other tools |
| Pipelines | Possible via integrations, not core | Core strength (Kubeflow Pipelines) |
| Ideal team stage | Early to scaling | Scaling to platform-grade |
When to Use MLflow, Kubeflow, or Both
Choose MLflow If…
You want quick wins and visibility into experimentation:
- Your team is iterating on models and needs tracking now
- You need a model registry to manage promotions
- You’re not ready to standardize everything on Kubernetes
- You want something straightforward that doesn’t require heavy platform investment
Choose Kubeflow If…
Your pain is operational scale and automation:
- You have multiple steps beyond training (data checks, approvals, deployment)
- You need robust scheduling and repeatable pipelines
- You’re already operating on Kubernetes
- You need an ML platform approach across teams
Use MLflow + Kubeflow Together If…
You want the best of both worlds:
- Kubeflow Pipelines orchestrate the workflow steps
- MLflow Tracking logs experiment runs, metrics, and artifacts
- MLflow Model Registry becomes the system of record for model versions and lifecycle
This combination is common because it cleanly separates concerns:
- Kubeflow handles automation
- MLflow handles traceability and model management
A Practical Reference Architecture (What It Looks Like in Real Life)
A realistic production MLOps setup using both tools might look like this:
1) Pipeline Orchestration (Kubeflow Pipelines)
Your pipeline stages could include:
- Data extraction
- Data validation (schema checks, missing values, outliers)
- Feature engineering
- Training
- Evaluation (offline metrics + bias checks)
- Registration (if model meets thresholds)
- Deployment trigger
2) Experiment Tracking and Registry (MLflow)
Within the training step, you:
- Log hyperparameters, metrics, charts
- Store artifacts (e.g., feature importance, model file)
- Register the candidate model version
3) Deployment and Serving (Kubernetes)
The deployment stage can:
- Pull the approved model from MLflow registry
- Deploy via Kubernetes-native serving (e.g., KServe or a custom service)
- Run smoke tests and/or canary rollout
4) Monitoring and Feedback
Post-deployment:
- Track inference latency and error rates
- Monitor data drift and model performance decay
- Trigger retraining pipelines based on thresholds (or schedules)
This is the path from “it works on my notebook” to “it runs reliably at scale.”
Practical Tips for Implementing MLOps with MLflow and Kubeflow
Start With the Bottleneck, Not the Tool
If your biggest issue is “we can’t reproduce results,” start with MLflow Tracking.
If your biggest issue is “deployments are manual and brittle,” consider Kubeflow Pipelines (or orchestration first).
Standardize What You Log
To unlock real value from MLflow, log consistently:
- Dataset version or snapshot ID
- Feature set version
- Code commit hash
- Model signature (inputs/outputs)
- Evaluation metrics and thresholds
This makes audits, debugging, and rollbacks dramatically easier.
Add Quality Gates in the Pipeline
Use pipeline steps that prevent “bad models” from shipping:
- Minimum metric thresholds (e.g., AUC must exceed X)
- Bias/fairness checks (if relevant)
- Data quality checks before training
- Regression tests against a baseline model
Keep Environments Reproducible
Even strong orchestration won’t save you from environment drift. Use:
- Containerized training steps
- Versioned dependencies
- Immutable artifacts for models and datasets
Common Challenges (And How to Avoid Them)
1) Overbuilding Too Early
A full Kubeflow platform can be overkill for a small team. If you only need experiment tracking and a basic registry, MLflow alone may be the practical starting point.
2) Fragmented Tooling
If metrics are in one system, models in another, and deployments elsewhere, teams lose trust. Define early:
- The “source of truth” for model versions (often the MLflow registry)
- The workflow owner (pipeline orchestration)
- Clear promotion rules
3) Missing Monitoring and Retraining Strategy
Many teams stop at deployment. But production ML systems drift. Plan for:
- Data drift detection
- Performance tracking on fresh labels (when available)
- Retraining triggers and approval workflows
FAQs (Optimized for Quick Answers)
What is the difference between MLflow and Kubeflow?
MLflow focuses on experiment tracking and model lifecycle management, while Kubeflow focuses on orchestrating ML pipelines and running ML workloads on Kubernetes. Many teams use them together: Kubeflow for automation, MLflow for tracking and registry.
Is MLflow a replacement for Kubeflow?
Not usually. MLflow is not a full pipeline orchestration platform in the same way Kubeflow is. MLflow can integrate into pipelines, but Kubeflow is purpose-built for Kubernetes-native workflow orchestration.
Can MLflow run on Kubernetes?
Yes. MLflow can be deployed on Kubernetes, and it’s a common approach when teams want scalable tracking and centralized artifact storage.
Do I need Kubeflow to do MLOps?
No. Many teams do effective MLOps with a simpler stack (e.g., MLflow + CI/CD + containerized deployments). Kubeflow becomes more valuable as automation needs and Kubernetes maturity increase.
What’s a practical MLOps stack for a growing ML team?
A common progression is:
1) MLflow for tracking and registry
2) Add pipeline orchestration (often Kubeflow Pipelines)
3) Add serving + monitoring + governance as production usage scales
