Airflow vs Kubeflow vs MLflow
Overview
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CLI (Node.js)
Manual
Detailed Comparison
Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed. | The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions. | MLflow is an open source platform for managing the end-to-end machine learning lifecycle. |
Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writting code that instantiate pipelines dynamically.;Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.;Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built in the core of Airflow using powerful Jinja templating engine.;Scalable: Airflow has a modular architecture and uses a message queue to talk to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity. | - | Track experiments to record and compare parameters and results; Package ML code in a reusable, reproducible form in order to share with other data scientists or transfer to production; Manage and deploy models from a variety of ML libraries to a variety of model serving and inference platforms |
Statistics | ||
GitHub Stars - | GitHub Stars - | GitHub Stars 22.8K |
GitHub Forks - | GitHub Forks - | GitHub Forks 5.0K |
Stacks 1.7K | Stacks 205 | Stacks 230 |
Followers 2.8K | Followers 585 | Followers 524 |
Votes 128 | Votes 18 | Votes 9 |
Pros & Cons | ||
Pros
Cons
| Pros
| Pros
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Integrations | ||
| No integrations available | No integrations available | |
What are some alternatives to Airflow, Kubeflow, MLflow?
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
PyTorch
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
GitHub Actions
It makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub. Make code reviews, branch management, and issue triaging work the way you want.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
TensorFlow.js
Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API
Apache Beam
It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.
Polyaxon
An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.
Streamlit
It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.
Zenaton
Developer framework to orchestrate multiple services and APIs into your software application using logic triggered by events and time. Build ETL processes, A/B testing, real-time alerts and personalized user experiences with custom logic.
