Recently, new technologies and tools for orchestrating tasks and Data Pipelines have exploded. Since there are so many of them, it can be challenging to decide which ones to employ and how they interact. In this guide, you will discuss Kubeflow vs Airflow differences and similarities.
Kubeflow is a Kubernetes-based end-to-end Machine Learning stack orchestration toolkit for deploying, scaling and managing large-scale systems. Airflow, on the other hand, is an open-source application for designing, scheduling, and monitoring workflows that are used to orchestrate tasks and Pipelines.
Selecting the right tool for your use case involves a lot of factors that need to be considered before making the decision. This post highlights some of the key differentiators that will help you take the Kubeflow vs Airflow decision easily. You will also discuss some of the common similarities between these tools. So, let’s dive deeper into the features and key components of Kubeflow and Airflow before you proceed forward to the Kubeflow vs Airflow comparison.
Table of Contents
- What is Kubeflow?
- What is Airflow?
- Kubeflow vs Airflow: Key Differentiators
- Similarities between Kubeflow & Airflow
What is Kubeflow?
Kubeflow is a Kubernetes-based open-source Machine Learning toolset. It converts stages in your Data Science process into Kubernetes jobs, giving your Machine Learning libraries, frameworks, pipelines, and notebooks a Cloud-native interface.
The “Kube” in Kubeflow derives from Kubernetes, a server orchestration tool. The term “Flow” was chosen to distinguish Kubeflow from other workflow schedulers such as ML Flow, Airflow, and others. Kubeflow works on Kubernetes clusters, either locally or in the cloud, allowing Machine Learning models to be trained on several computers at once, greatly reducing the time it takes to train a model.
Kubeflow focuses on precise project management as well as in-depth project monitoring and analysis. Engineers and Data Scientists can now build a fully working pipeline with segmented processes. Kubeflow’s capabilities, such as the ability to run JupyterHub servers, which let several people work on a project at the same time, have proven to be quite useful.
Key Features of Kubeflow
Google’s Kubeflow offers many features and improvements that make MLOps simple and straightforward. Let’s look at some of Kubeflow’s prominent features:
- Comprehensive Dashboard: Engineers can use K8s to design, deploy, and monitor their models in production thanks to a central dashboard with multi-user isolation.
- Multi-Model Serving: KFServing, Kubeflow’s model serving component, is designed to serve several models at once. With an increase in the number of queries, this strategy quickly uses up available cluster resources.
- ML Libraries, Frameworks & IDEs: Kubeflow is interoperable with the data science libraries and frameworks such as Scikit-learn, TensorFlow, PyTorch, MXNet, XGBoost, etc. Users of Kubeflow v1.3+ can launch Jupyter notebook servers, RStudio, or VSCode, straight from the dashboard, with the appropriate storage allocated.
- Monitoring & Optimizing Tools: Tensorboard has been integrated into Kubeflow’s service which helps in visualizing your ML training process. In addition, Kubeflow incorporates Katib. It is a hyperparameter tweaking tool that runs pipelines with various hyperparameters to get the optimal ML model.
Understanding the Kubeflow Components
Kubeflow is a free and open-source Machine Learning tool that lets you leverage ML pipelines to manage complex Kubernetes operations. Kubeflow is made up of the following logical components:
- Central Dashboard: The Kubeflow deployment comes with a central dashboard that gives you easy accessibility to all of the Kubeflow components installed in your cluster. The following functions are included in the dashboard: Shortcuts to specific actions, a list of recent pipelines and notebooks, and metrics, providing a single view of your jobs and cluster, a home for the UIs of the cluster’s running components, such as Pipelines, Katib, Notebooks, and more, and a registration flow that prompts new users to set up their namespace if necessary.
- Kubeflow Notebooks: Kubeflow Notebooks allows you to host web-based development environments within your Kubernetes cluster by encapsulating them in Pods. Kubeflow deployment provides management and spawning services for your notebooks. There can be several notebook servers in a Kubeflow deployment, and each notebook server can have multiple notebooks.
- Kubeflow Pipelines: Kubeflow Pipelines is a Docker-based platform for creating and deploying portable, scalable ML processes. It has a user interface for managing tasks, an engine for scheduling multi-step Machine Learning processes, an SDK for defining and manipulating pipelines, and notebooks for interacting with the system through SDK.
- KFServing: On Kubernetes, KFServing enables serverless inferencing. It also gives ML frameworks like PyTorch, TensorFlow, scikit-learn, and XGBoost computationally efficient and high-abstraction interfaces.
- Katib: Katib is a Kubernetes-based project for Machine Learning automation (AutoML). Hyperparameter tweaking, early halting, and neural architecture search are all supported by Katib. It also supports numerous ML frameworks natively.
- Training Operators: This allows you to train Machine Learning models using operators. For instance, TensorFlow training (TFJob) conducts TensorFlow model training on Kubernetes, while XGBoost Training (XGBoostJob) trains a model with XGBoost.
- Multi-Tenancy: Users can access and modify the Kubeflow components and model objects in their configuration, which simplifies user activities. Authentication, authorization, administrator, user, and profile are key aspects in Kubeflow’s multi-user isolation.
Explore more about Kubeflow here.
What is Airflow?
Apache Airflow is an open-source application for creating, scheduling, and monitoring workflows. It’s one of the most trusted solutions for coordinating activities or Pipelines among Data Engineers. Airflow has evolved into one of the most powerful open source Data Pipeline systems currently available. It was designed to be a flexible job scheduler. Its use, on the other hand, does not end there. It’s also used to train Machine Learning models, send out notifications, keep track of systems, and fuel a variety of API actions.
Airflow allows users to create workflows as DAGs (Directed Acyclic Graphs) of tasks. Visualizing pipelines in production, monitoring progress, and resolving issues is a snap with Airflow’s robust User Interface. It connects to a variety of data sources and can send notifications to users through email or Slack when a process is completed or fails. Since it is distributed, scalable, and adaptable, it is ideal for orchestrating complicated Business Logic.
Key Features of Airflow
Many companies, including Slack, Robinhood, Freetrade, 9GAG, Square, Walmart, and others, employ Apache Airflow. Let’s have a look at some of the outstanding features that set Airflow apart from the competition:
- Easy to Use: An Airflow Data Pipeline can be readily set up by anybody familiar with the Python programming language. Users can develop ML models, manage infrastructure, and send data with no restrictions on pipeline scope.
- Robust Pipelines: Airflow pipelines are simple to implement. Its core is the advanced Jinja template engine, which allows you to parameterize your scripts. Furthermore, owing to the advanced scheduling semantics, users can run pipelines at regular intervals.
- Extensive Integrations: Airflow offers a big pool of operators ready to operate on the Google Cloud Platform, Amazon Web Services, and a variety of other third-party platforms. As a consequence, integrating it into current infrastructure and scaling up to next-generation technologies is clear and simple.
- Pure Python: Users can create Data Pipelines with Airflow by leveraging basic Python features like data time formats for scheduling and loops for dynamically creating tasks.
Understanding the Airflow Components
Apache Airflow is comprised of 5 key components:
- Web Server: It is the User Interface of Airflow. At its heart, this is a Flask app that shows the status of your jobs, gives a database interface, and reads logs from a remote file store. It’s not only a fantastic way to get an overview of past and current DAGs, but it’s also a terrific place to troubleshoot errors and resubmit failed jobs.
- Scheduler: The scheduler is at the core of Airflow, constantly polling the database to keep track of the progress of each job and ensure that the executor is doing the appropriate work.
- Executor: When a job is selected for execution, the scheduler distributes the work to an executor. Airflow supports a variety of executors, including Sequential executors, Local executors, Celery executors, and more.
- Workers: These are the processes that carry out task logic and are specified by the Executor in use.
- Metadata Database: Airflow is supported by a database that maintains metadata as well as all of the previous DAG runs. It controls how the other components interact, keeps Airflow states, and is where all processes read from and write to.
Do you want to learn more about Apache Airflow’s other significant features and benefits? Refer to the Airflow Official Page.
Read along the next section, to understand the Kubeflow vs Airflow key differences and similarities.
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Kubeflow vs Airflow: Key Differentiators
Now, that you have gained a good understanding of the features and basic underlying components of Kubeflow and Airflow, let’s discuss the differences between Kubeflow and Airflow. In this section, you will explore the 4 critical differentiators that will help in Kubeflow vs Airflow decision.
- Kubeflow vs Airflow: Function
- Kubeflow vs Airflow: Kubernetes Requirement
- Kubeflow vs Airflow: GitHub Popularity & Support
- Kubeflow vs Airflow: Use Case
1) Kubeflow vs Airflow: Function
Airflow is a generic task orchestration tool, whereas Kubeflow concentrates on Machine Learning activities like experiment tracking. An experiment in Kubeflow is a workspace that allows you to experiment with alternative pipeline setups. Kubeflow is divided into 2 components: Kubeflow and Kubeflow Pipelines. The latter enables you to specify DAGs, although it focuses more on deployment and model serving than on general operations.
2) Kubeflow vs Airflow: Kubernetes Requirement
Kubeflow is designed to run especially on Kubernetes. It works by letting you configure your Machine Learning components on Kubernetes.
Working with Airflow, on the other side, does not necessitate the use of Kubernetes. However, it’s worth noting that if you want to run Airflow on Kubernetes, you can do so using the Kubernetes Airflow Operator.
3) Kubeflow vs Airflow: GitHub Popularity & Support
In late 2017, Google launched Kubeflow, an open-source project to manage its internal Machine Learning pipelines written in Tensorflow and running on Kubernetes.
Airbnb, on the other hand, started using Airflow, an open-source workflow management platform for Data Engineering pipelines, in October 2014 as a way to manage the company’s increasingly complicated processes.
Airflow is used by significantly more Engineers and businesses than Kubeflow. On Github, for example, Airflow has more forks and stars than Kubeflow. Airflow is also more often used in enterprise and developer stacks than Kubeflow. Slack, Airbnb, and 9GAG are just a few of the well-known firms that use Airflow. Since Airflow is so widely used, users can get quick help from the community.
4) Kubeflow vs Airflow: Use Case
If you require a mature, comprehensive ecosystem that can handle a wide range of jobs, Airflow is the way to go. However, if you currently use Kubernetes and want additional out-of-the-box patterns for Machine Learning solutions, Kubeflow is the right choice for you.
Selecting the right tool for your project is not a piece of cake. The above Kubeflow vs Airflow comparison will help make your selection easier. However, the Kubeflow vs Airflow decision involves a lot more factors such as team size, team skills, use case, & others.
Similarities between Kubeflow & Airflow
Despite their numerous differences, Kubeflow and Airflow have certain elements in common. The following are some of the similarities between the 2 tools:
- ML Orchestration: Kubeflow and Airflow are both capable of orchestrating Machine Learning pipelines, but they take quite different methods as discussed above.
- Open-Source: Kubeflow and Airflow are both open-source solutions. That is, they may be accessed by anybody, at any time, from anywhere. Both offer active communities and users, however, Airflow has a larger user base than Kubeflow.
- User Interface(UI): Both have an interactive UI. The central dashboard is the interface in Kubeflow that offers simple access to all Kubeflow components installed in your cluster. The user interface in Airflow gives you a complete picture of the status and logs of all tasks, both finished and in progress.
- Python: Python is leveraged by both of them. For example, you can construct workflows in Airflow using Python functionalities. In Kubeflow, you can also define tasks using Python.
In a nutshell, choosing the best orchestration tool for the use cases can be quite difficult. This post helped you make your Kubeflow vs Airflow decision easier. You not only discovered the Kubeflow vs Airflow differences but also discussed some of the similarities shared. In addition, you gained a basic understanding of key features and components of Kubeflow and Airflow.
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