TFX vs KubeFlow

My understanding is that TFX and KubeFlow are both used to build ML Pipelines. If both are offered by Google, what is the fundamental difference between the two?

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Kubeflow is restricted to kubernetes and is meant to be an orchestrator for an ML task like deployment. TFX can be run within kubeflow context.

TFX on the other hard does the schema inference and everything that’s ML related. It can work with other frameworks as well (see tfx-addons).

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Hello @saurabhshrivastava,

Building on what @balaji.ambresh shared, let me break down your question to provide a clearer understanding.

In real-world scenarios, your specific requirements and constraints will play a crucial role in guiding your choice between TFX and Kubeflow.

As mentioned by Balaji, Kubeflow is more general and is specifically tailored for Kubernetes. It is designed to be framework-agnostic, meaning it can work with various ML frameworks such as TensorFlow, PyTorch, and others.

On the other hand, TFX has a narrower focus. It is centered around providing a comprehensive platform for deploying production-ready machine learning models using TensorFlow. TFX is tightly integrated with TensorFlow, making it an excellent choice for projects heavily relying on TensorFlow for their ML tasks.

While TFX is closely tied to TensorFlow, Kubeflow offers a more flexible approach. It is designed to be framework-agnostic, giving users the freedom to utilize different ML frameworks within the same platform. Whether it’s TensorFlow, PyTorch, MXNet, or others, Kubeflow provides versatility.

There’s no one-size-fits-all rule that dictates choosing TFX or Kubeflow. Your decision will depend on the specific environment, requirements, and constraints you are dealing with.

I hope this breakdown provides more clarity. Feel free to ask for further clarification if needed.


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Thanks Jamal for your detailed response. For further clarifications:

  1. From your response it looks like a person should choose either TFX or Kubeflow, depending on their need. This makes them sound like two different products that help do the same thing, but in different environments (based on frameworks or hardware set up). But in the lecture video it seemed like these products are complementing each other. TFX was offloading certain responsibilities to Kubeflow (or Airflow if user wants). So are they “competing” or “complimenting” products?
  2. Should we think of “ML Platform” and “Orchestrator” as same thing or they are different? It seems like TFX is usually called ML Platform and KubeFlow is referred as Orchestrator. How should I understand these two concepts?
  3. On the similar lines are ML Flow, KubeFlow or AirFlow similar products, just coming from different companies? I have heard they are are different in terms of functionalities, but it confuses me more. While reading about this more I felt that Airflow is more like Orchestrator and KubeFlow is like a ML platform (but this contradicts with point 2 above).
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Hello @saurabhshrivastava,

Sorry for late reply i will try to address your points.

Let’s start with Competition vs. Complementarity part:

  • TFX and Kubeflow can be seen as both competing and complementing, depending on the perspective. TFX focuses more on providing a comprehensive ML platform specifically tailored for TensorFlow-based workflows. On the other hand, Kubeflow is a broader framework-agnostic platform that can handle various ML frameworks, and it can serve as an orchestrator for TFX workflows.

  • In certain scenarios, TFX can leverage Kubeflow (or Airflow) for orchestration, effectively complementing each other. TFX might offload certain responsibilities to external orchestrators, showcasing a collaborative approach.

Now it takes us to the second part of which is ML Platform vs. Orchestrator:

  • First the terms “ML Platform” and “Orchestrator” refer to different components in the ML pipeline.

  • TFX, often referred to as an ML Platform, provides tools and components for end-to-end machine learning, including data ingestion, feature engineering, model training, and deployment.

  • Orchestrators like Kubeflow, Airflow, or others handle the coordination and scheduling of the various tasks within the ML pipeline. They ensure that different components are executed in the right order and manage the flow of data between them.

Maybe you are still confused now and think that Kubeflow is an “ML platform” right?

Okay you are right it’s an platform but i will try to define what is the meaning of platform first:

Platform: refers to a comprehensive set of tools, frameworks, and services that facilitate the development, deployment, and management of machine learning models and workflows

Now maybe you got it! both TFX and Kubeflow are ML platforms but server different things.

I will try to make a small comparison between ML Flow, KubeFlow and Airflow:

  • Focus:
  1. MLflow is more focused on the machine learning lifecycle, emphasizing experiment tracking, model packaging, and deployment.

  2. Kubeflow is specifically tailored for machine learning on Kubernetes, covering aspects such as orchestration, hyperparameter tuning, and model serving.

  3. Apache Airflow is a general-purpose workflow orchestration tool but is often used for managing data engineering and ML workflows.

I hope it’s more clear for you now and feel free to ask for more clarifications if needed.


This is such a nice explanation Jamal. It is very helpful. Thank you!


You’re Welcome!!!
Happy learning!!

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