A Brief Comparison of Kubeflow vs Airflow
There has been an explosion in new technologies and tools for managing tasks and data pipelines in recent years. 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, especially because selecting the right tool for your use case involves many factors that all need to be given due consideration.
In a series of new guides, we’re going to compare the Kubeflow toolkit with a range of others, looking at their similarities and differences, starting with Kubeflow vs Airflow.
Kubeflow is a Kubernetes-based end-to-end machine learning (ML) stack orchestration toolkit for deploying, scaling, and managing large-scale systems. Meanwhile, Airflow is an open-source application for designing, scheduling, and monitoring workflows for orchestrating tasks and pipelines.
In this comparison, we’re going to look at the main differentiators that will help you make a decision between Kubeflow vs Airflow. 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, ML Flow, 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 Airflow?
Apache Airflow is an open-source application for building, scheduling, and monitoring workflows. Today, it is one of the most trusted solutions for coordinating activities or pipelines among ML teams.
Over the years, Airflow has evolved into one of the most powerful open-source data pipeline systems available. Initially designed as a flexible job scheduler, its use cases don’t end there. Airflow is also used to train ML models, send notifications, keep tabs on systems, and fuel a variety of API actions.
The most notable feature of Airflow is that it enables users to create workflows as Directed Acrylic Graphs (DAGs) of tasks, making it easy to visualize pipelines in production, monitor progress, and resolve issues with a robust UI. The tool connects to a variety of data sources and can send notifications to users through email or Slack when a process is completed or fails.
The main components and features of Airflow include:
- Scheduler—The scheduler monitors tasks and DAGs, triggers scheduled workflows, and submits tasks to the executor to run. It’s built to run continuously in the Airflow production environment.
- Executors—Executors are mechanisms that run task instances. Executors have a common API, and they can be swapped based on installation requirements. You can only have one executor configured per time.
- Webserver: A user interface that displays the status of your jobs and allows you to view, trigger, and debug DAGs and tasks. It also helps you to interact with the database and read logs from the remote file store.
- Ease of use— An Airflow Data Pipeline can be set up quickly by anybody who is familiar with Python. Users can develop ML models, manage infrastructure, and send data with no restrictions on scope.
- Integrations—Airflow offers a large selection of integrations that include Google Cloud Platform, Amazon Web Services, and a variety of other third-party platforms. As a result, integrating it into current infrastructure and scaling up to next-gen technologies is simple.
- Solid pipelines—Airflow pipelines are simple to implement, and users can run pipelines at regular intervals.
- Python core—Users can create data pipelines with Airflow by using basic Python features such as data time formats for scheduling and loops for creating tasks.
Unlike Kubeflow, Airflow is solely focused on a single purpose, and this means that the Airflow components listed above are much lower level than those listed for Kubeflow.
Kubeflow vs Airflow similarities
Kubeflow and Airflow have many things in common. Similarities between the two toolkits include:
- Both tools can be used to orchestrate ML pipelines, but there are different approaches as we will explore when we look at the differences.
- Both Kubeflow and Airflow are open source. As such, they both have rich developer communities who actively contribute to the toolkits to improve them and add additional functionality through third-party modifications. When comparing the two, Airflow has the bigger community.
- Both Kubeflow and Airflow have a UI in the form of a central dashboard that provides easy access to all components deployed in a cluster. In Airflow, the user interface provides a full overview of the status and logs of all tasks, both completed and ongoing.
- Both Kubeflow and Airflow utilize Python. In Airflow, for example, Python features can be used to create workflows whereas with Kubeflow, Python can be used to define tasks.
Kubeflow vs Airflow differences
Although there are many similarities, there are fundamental differences deep down.
The main difference between the two is that Kubeflow was created by Google to organize its internal ML processes while Airflow was built by Airbnb to automate software workflows. As such, there are critical differences that stem from these differences in core purpose.
- Airflow is solely a pipeline orchestration platform whereas Kubeflow has functionality in addition to orchestration. This is because Kubeflow focuses on ML learning tasks such as experiment tracking.
- Unlike Kubeflow, Airflow doesn’t offer best practices for ML. Instead, it requires you to implement everything yourself. This is because Airflow wasn’t built with ML pipelines in mind despite it being used for pipelines today.
- More engineers and companies use Airflow than Kubeflow. Just take a look at the difference between the two in terms of the number of forks and stars they each have for an idea of how big the difference in community size is.
- Kubeflow runs exclusively on Kubernetes and works by allowing you to arrange ML components on Kubernetes. Meanwhile, you don’t need Kubernetes to work with Airflow.
Kubeflow vs Airflow summed up
Kubeflow and Airflow are comparable insofar as that with both of them, you can build and orchestrate DAGs. In many ways, the similarities stop there. This means that choosing the best orchestration tool for the use cases can be quite difficult.
We hope that our brief comparison has helped you to make your Kubeflow vs Airflow decision and has provided you with a basic understanding of the key features and components of Kubeflow and Airflow.
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!