A Brief Comparison of Kubeflow vs Argo

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 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. 

What is Kubeflow? 

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:

• Kubeflow pipelines—Kubeflow empowers teams to build and deploy portable, scalable ML workflows based on Docker containers. It includes a UI to manage jobs, an engine for scheduling multi-step ML workflows, an SDK to define and manipulate pipelines, and notebooks to interact with the system.

• KFServing—This enables serverless inferencing on Kubernetes and provides performant and high abstraction interfaces for ML frameworks such as PyTorch, TensorFlow, and XGBoost. 

• Notebooks—Kubeflow deployment provides services for managing and spawning Jupyter notebooks. Each Kubeflow deployment can include several; notebook servers and each notebook server can include multiple notebooks.

• Training operators—This enables teams to train ML models through operators. For example, it provides TensorFlow training that runs TensorFlow model training on Kubernetes for model training. 

• Multi-model serving—KFServing is designed to serve several models at once. With an increase in the number of queries, this can quickly use up available cluster resources.

What is Argo?

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:

• Workflow—The most important component in Argo, workflow serves two purposes. First, it defines the workflow to be executed. Second, it stores the state of the workflow. 

• Templates—Argo has two main categories of templates: Template Definitions and Template Invocators. The former defines the work to be done while the latter is used to invoke or call other templates and provide execution control. The template types under this category include the Step template, which allows you to define tasks in a series of steps, and the DAG template, which allows you to define tasks as a graph of dependencies.

• Workflow executor—A method that executes container, workflow executors conform to a specific interface that allows Argo to carry out specific actions, such as collecting artifacts or managing container lifecycles. 

• Artifact repository—The place where all artifacts are stored. 

Kubeflow vs Argo similarities

Kubeflow and Argo have a few key similarities:

• Both Kubeflow and Argo have pipeline orchestration functionality and they take a similar approach to pipelines. In both platforms, pipeline steps are independent containers that are run, and data flow is defined via pipeline configuration.

• With both Kubeflow and Argo, ML teams who have previously built CI/CD systems will find the platforms very familiar. You can define pipelines using YAML in both, for example. However, Kubeflow also allows the use of a Python interface in addition to YAML, which some teams may prefer. 

• Kubeflow and Argo are both open-source software with their own dedicated communities behind them. 

• Both platforms are based on Kubernetes. 

Kubeflow vs Argo differences

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. 

Kubeflow vs Argo summary

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. 

Qwak as an alternative to Kubeflow and Argo

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!