Updated September 2026. Corrected the model registry and metadata details, and added what changed since the original post: MLflow 3, ZenML’s Model Control Plane, and TFX narrowing its focus. For a head-to-head of the two most compared tools, see TFX vs MLflow.
Machine learning operations (MLOps) have become indispensable for modern data-driven organizations, requiring robust frameworks to streamline ML workflows. This article compares four prominent MLOps frameworks — TensorFlow Extended (TFX), Kubeflow, ZenML, and MLflow — highlighting their features, functionalities, and suitability for various use cases.
The most important thing to know before comparing them: they are not all the same kind of tool. TFX is a pipeline framework, Kubeflow is a platform on Kubernetes, ZenML is a pipeline abstraction over your existing stack, and MLflow is a tracking and model lifecycle layer. Most production setups combine two of them.
What is MLOps?
MLOps refers to the set of practices and tools used to streamline the entire machine learning lifecycle, including data preparation, model training, deployment, and monitoring. It fosters collaboration among data scientists, ML engineers, and operations professionals to ensure efficient and scalable ML workflows.
TensorFlow Extended (TFX)
TFX, developed by Google, is a framework built on TensorFlow for end-to-end production ML pipelines. Its core components include:
- ExampleGen: Data ingestion
- StatisticsGen / SchemaGen / ExampleValidator: Data validation with TensorFlow Data Validation
- Transform: Data preprocessing that is exported into the serving graph
- Trainer: Model training
- Evaluator: Model validation against a baseline with TensorFlow Model Analysis
- Pusher: Deployment
Every component records its artifacts and executions in ML Metadata (MLMD), which gives TFX automatic lineage. TFX has no dedicated model registry: the Pusher deploys “blessed” models to TensorFlow Serving, Vertex AI (including Vertex AI Model Registry) or a filesystem. Pipelines run on Kubeflow Pipelines, Vertex AI Pipelines, Airflow or Apache Beam.
2026 status: actively released (1.21, June 2026, aligned with TensorFlow 2.21), but increasingly focused on TensorFlow workloads on Google Cloud. The Estimator API and the KFP v1 runner are gone or deprecated.
Kubeflow
Kubeflow is an open-source MLOps platform, now a CNCF project, designed to run ML workloads on Kubernetes with modularity, portability, and scalability. Its key components include:
- Kubeflow Pipelines (KFP): Workflow orchestration
- Notebooks: Experimentation environment
- Katib: Hyperparameter tuning
- Trainer: Distributed training on Kubernetes
- KServe: Model serving
- Model Registry: Model versions and metadata
Kubeflow supports multiple ML frameworks like TensorFlow and PyTorch. KFP tracks artifacts and lineage in ML Metadata; for infrastructure monitoring you typically add Prometheus and Grafana.
2026 status: KFP 2.x is the stable core. You can adopt individual components (most teams start with Pipelines or KServe) instead of the whole platform.
ZenML
ZenML is a Python-first MLOps framework focused on reproducibility and portability. You write pipelines once and run them on a “stack” of your choice — local, Kubeflow, Vertex AI, SageMaker, Airflow and others. It offers:
- Portable pipelines decoupled from the orchestrator
- Artifact and data versioning
- Experiment tracking through integrations (MLflow, Weights & Biases)
- Model Control Plane for model versions, stages and their linked artifacts
ZenML is no longer an early-stage project: it ships frequent releases (0.97 as of September 2026) and has grown model management and support for LLM and agent workflows. Some features, such as the model dashboard, are part of ZenML Pro.
MLflow
MLflow is a framework-agnostic platform for model development, deployment, and management. Its core components are:
- Tracking: Logs experiments (params, metrics, artifacts)
- Projects: Packages reproducible runs
- Models: Handles model packaging and deployment
- Model Registry: Tracks model versions, aliases and lifecycle
MLflow 3 added LoggedModel as a first-class entity and a GenAI toolkit: tracing, a prompt registry, and LLM evaluation. It also removed MLflow Recipes, so MLflow has no pipeline orchestration of its own — pair it with Airflow, Kubeflow Pipelines, ZenML or similar.
Comparison of Frameworks
| Feature | TFX | Kubeflow | ZenML | MLflow |
|---|---|---|---|---|
| Type of tool | Pipeline framework | Kubernetes ML platform | Pipeline abstraction layer | Tracking and model lifecycle |
| Ease of Use | Complex | Complex | Simple | Simple |
| Scalability | High | Very High | Depends on the stack | High |
| Framework Support | TensorFlow | TensorFlow, PyTorch, etc. | Multiple | Multiple |
| Pipeline Orchestration | Yes (via KFP, Vertex, Airflow, Beam) | Yes (KFP) | Yes (on any supported orchestrator) | No |
| Data Validation | Yes (TFDV) | No (bring your own) | Via integrations | No |
| Metadata & Lineage | ML Metadata | ML Metadata | Built in | Runs, LoggedModels, traces |
| Model Registry | No (deploys to serving / Vertex AI Model Registry) | Yes (Kubeflow Model Registry) | Yes (Model Control Plane) | Yes |
| Serving | TensorFlow Serving, Vertex AI | KServe | Via integrations | Built-in model serving |
| LLM / GenAI Support | No | Partial (KServe) | Yes | Yes (tracing, prompts, evaluation) |
Conclusion
Choosing the right MLOps framework is crucial for the success of ML projects. Each framework offers unique advantages:
- TFX: Best for TensorFlow-based production pipelines on Google Cloud, where data validation and evaluation gates matter.
- Kubeflow: Ideal for large-scale ML on Kubernetes when you have a platform team to run it.
- ZenML: Suitable for teams that want portable, reproducible pipelines without committing to one orchestrator.
- MLflow: The default system of record for experiments and models, and the strongest choice of the four for LLM applications.
A common production combination is an orchestrator (Kubeflow Pipelines, Vertex AI Pipelines, Airflow or ZenML) plus MLflow for tracking and the registry. Comparing TFX and MLflow specifically? Read TFX vs MLflow: Which One Do You Actually Need?.
Further reading
- Kubeflow Documentation. (n.d.). Retrieved from Kubeflow Docs
- MLflow Documentation. (n.d.). Retrieved from MLflow Docs
- MLflow 3 release overview. Retrieved from mlflow.org
- Munteanu, A. (2022, December 14). What is MLOps? Ubuntu Blog. Retrieved from Ubuntu
- TensorFlow Extended (TFX). (n.d.). Retrieved from TensorFlow
- ZenML Documentation. (n.d.). Retrieved from ZenML Docs