Kubeflow, created by Google in 2018, and Metaflow, created by Netflix in 2019, are powerful machine learning operations (MLOps) platforms that can be used for experimentation, development, and production.
Indeed, there’s no shortage of similar MLOps tools available on the market right now that all promise to do one thing: make the lives of ML teams easier. As a result of the explosion in growth of the number of available MLOps tools, it can be challenging to decide which ones to use and understand how they interact with one another.
Some tools like Google’s Kubeflow have been built specifically for MLOps while others are designed for more general-purpose applications and are not built specifically for ML workflows, such as Argo.
In a series of new guides, we’re comparing the Kubeflow toolkit with a range of others, looking at their similarities and differences. This time, we’re looking at Kubeflow vs Metaflow.
Kubeflow is a Kubernetes-based end-to-end machine learning (ML) stack orchestration toolkit for deploying, scaling, and managing large-scale systems. The Kubeflow project is dedicated to making ML on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up the best possible OSS solutions.
Metaflow is a ‘human-friendly’ Python library that helps scientists and ML engineers build and manage data science projects. It was originally developed by Netflix to boost the productivity of data science teams who work on a variety of different projects.
In this comparison, we’re going to look at the main differentiators that will help you decide between Kubeflow vs Argo. We’re also going to cover some of the similarities that exist between the two.
Kubeflow is a free and open-source ML platform that allows you to use ML pipelines to orchestrate complicated workflows running on Kubernetes.
It’s based on the Kubernetes open-source ML toolkit and works by converting stages in your data science process into Kubernetes ‘jobs’, providing your ML libraries, frameworks, pipelines, and notebooks with a cloud-native interface.
Kubeflow works on Kubernetes clusters, either locally or in the cloud, which enables ML models to be trained on several computers at once. This reduces the time it takes to train a model.
Some of the features and components of Kubeflow include:
Metaflow, as we’ve already discussed, is a Python library that enables teams to build production ML. Netflix initially developed it to improve the productivity of data scientists who build and maintain different types of machine learning models. However, it is a much more focused tool and the major concepts within it revolve around pipelines and orchestration.
Metaflow is made up of five major components. These are:
Kubeflow and Metaflow are both tools that operate in the MLOps space. Some similarities that exist between Kubeflow vs Metaflow include:
While Kubeflow attempts to capture the entire ML development process with hosted notebooks, serving, and other functionality on top of pipeline automation, Metaflow is more focused on orchestrated pipelines. Here are some ways that the two tools differ:
There’s a huge difference in scope between the two tools. Which one is the right for your team will therefore depend on any tools you’ve already adopted.
If you haven’t adopted any tooling yet, Kubeflow is likely to be a useful solution whereas if you’re looking for a tool to handle production pipelines only, Metaflow would be the better option.
Both tools have different approaches to pipelines. Metaflow pipelines are Python methods passing data to one another, and this makes them relatively easy to build. There is a drawback though, and that is that Metaflow can be tricky to work with when you have unconventional datasets.
In Kubeflow, however, steps run in separate containers and communicate via files. This is a much more versatile approach because steps can have their own dependencies, and any kind of data is easy to transfer in files. Kubeflow doesn’t support Python communication between components, which may be a limiting factor.
The approach that works best for your ML teams will depend entirely on your use case and preferences.
Kubeflow architecture provides Kubernetes ‘under the hood’ This helps to solve problems such as cloud deployment and migration because Kubernetes is open-source and can be installed on any cloud.
In addition, DevOps and MLOps teams are often more familiar with Kubernetes, and there’s a wider range of third-party tools available for Kubernetes cluster monitoring.
Metaflow locks in Amazon Web Services whereas Kubeflow has unlimited cloud deployment, such as GCP, Azure, or anything that runs on Kubernetes.
By deploying and utilizing machine learning with Kubeflow or Metaflow, teams can unlock support for the most common ML scenarios, such as managing code, data, and dependencies for experiments.
In this article, we’ve highlighted and compared some of the critical similarities and differences between Kubeflow and Metaflow to help you decide between the two very different but very powerful platforms.
If you’ve got a larger team that would benefit from a unified workspace where the entire team can experiment with and deploy machine learning models into production, Kubeflow is likely to be your best choice.
That said, if you’re more interested in building production pipeline but you’ve already got tooling in place for most things, Metaflow is going to be your best choice. The power of Metaflow is the fact that its approach is opinionated, but at the same time this can mean that it might not fit every use case. When it does, however, it’s a powerful tool that’s very easy to work with — and remember, it doesn’t require Kubernetes.
Instead of using either of these, though, why not use a tool like Qwak?
Qwak is a robust MLOps platform that provides a similar feature set to Kubeflow in a managed service environment that enables you to skip the maintenance and setup requirements.
Our full-service ML platform enables teams to take their models and transform them into well-engineered products. Our cloud-based platform removes the friction from ML development and deployment while enabling fast iterations, limitless scaling, and customizable infrastructure.