Organizations are rapidly investing in MLOps to enhance their productivity and create cutting-edge machine learning (ML) models. MLOps helps to streamline the ML lifecycle by automating repeatable tasks and providing best practices to help ML teams collaborate more effectively.
As a result of the growth of MLOps in recent years, there has been an explosion in new technologies and tools for managing tasks and data pipelines. There are now so many of them, in fact, that it can be challenging to decide which ones to use and understand how they interact with one another.
Yet, one of the biggest concerns for many firms is finding the most suitable platform to manage their automated workflows. Some are looking toward tools like Kubeflow which have been built specifically for MLOps while others are looking at more general-purpose orchestrators such as Argo, which, while not specifically built for ML workflows, can be adapted for them.
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 Argo.
Kubeflow is a Kubernetes-based end-to-end machine learning (ML) stack orchestration toolkit for deploying, scaling, and managing large-scale systems while Argo is an open-source container-native workflow engine used for orchestrating parallel jobs on Kubernetes.
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 common 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.
The “Kube” in Kubeflow is derived from Kubernetes, whereas “flow” was chosen to distinguish Kubeflow from other workflow schedulers such as Airflow, MLflow, and others that will be covered in later guides. 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.
Kubeflow is made up of many features and components, including:
Argo is an open-source container built on Kubernetes. It’s a container-native workflow engine that’s used for orchestrating parallel jobs. Created by Applatex, a subsidiary of Intuit, Argo can handle tens of thousands of workflows at once with 1,000 steps each. These step-by-step procedures have dependencies and are referred to as Directed Acyclic Graphs (DAGs).
Argo is made up of many features and components, including:
Kubeflow and Argo have a few key similarities:
Both Kubeflow and Argo were built within tech communities. In the case of Kubeflow, it originated with Google while Argo originated with Intuit.
Kubeflow is an end-to-end MLOps platform for Kubernetes, while Argo is the workflow engine for Kubernetes. Meaning Argo is purely a pipeline orchestration platform used for any kind of DAGs.
Although it’s possible to use Kubeflow to orchestrate ML pipelines, it doesn’t offer any other ML-specific features such as experiment tracking. On the other hand, however, Kubeflow does try to capture the full model lifecycle under a single platform.
Kubeflow can technically be seen as a part of Kubeflow because Kubeflow pipelines can orchestrate tasks like Argo.
Although both Kubeflow and Argo are open-source solutions, ML teams will gravitate towards the one that comes with more capabilities, especially since both solutions share a Kubernetes dependency at their core. However, with added features comes added complexity.
Let’s say for instance that you’re already using a workflow orchestrator such as Argo, and you’re looking to implement ML pipelines. The logical choice here would be to continue with your orchestrator of choice. However, if you’re instead looking for a comprehensive platform that centralizes everything and will deliver benefits as your team grows, Kubeflow might be a better choice.
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.
Want to find out more about how Qwak could help you deploy your ML models effectively? Get in touch for your free demo!
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