What is Kubeflow?
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.
Kubeflow is a tool in the Machine Learning Tools category of a tech stack.
Kubeflow is an open source tool with 9.1K GitHub stars and 1.4K GitHub forks. Here’s a link to Kubeflow's open source repository on GitHub
Who uses Kubeflow?
11 companies reportedly use Kubeflow in their tech stacks, including Hepsiburada, bigin, and Beat.
64 developers on StackShare have stated that they use Kubeflow.
Kubernetes, TensorFlow, Jupyter, Pipelines, and Google AI Platform are some of the popular tools that integrate with Kubeflow. Here's a list of all 5 tools that integrate with Kubeflow.
Kubeflow Alternatives & Comparisons
What are some alternatives to Kubeflow?
See all alternatives
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.
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
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.
An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.