Kubeflow

101
330
+ 1
8
MLflow

76
250
+ 1
5
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Kubeflow vs MLflow: What are the differences?

Developers describe Kubeflow as "Machine Learning Toolkit for Kubernetes". 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. On the other hand, MLflow is detailed as "An open source machine learning platform". MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

Kubeflow and MLflow can be categorized as "Machine Learning" tools.

Kubeflow and MLflow are both open source tools. It seems that Kubeflow with 6.93K GitHub stars and 1K forks on GitHub has more adoption than MLflow with 20 GitHub stars and 11 GitHub forks.

Pros of Kubeflow
Pros of MLflow

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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.

What is MLflow?

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
What companies use Kubeflow?
What companies use MLflow?

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What tools integrate with Kubeflow?
What tools integrate with MLflow?

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What are some alternatives to Kubeflow and 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.
Apache Spark
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.
Airflow
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.
Polyaxon
An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.
Argo
Argo is an open source container-native workflow engine for getting work done on Kubernetes. Argo is implemented as a Kubernetes CRD (Custom Resource Definition).
See all alternatives
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