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Kubeflow vs Propel: 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, Propel is detailed as "Machine learning for JavaScript". Propel provides a GPU-backed numpy-like infrastructure for scientific computing in JavaScript.
Kubeflow and Propel can be categorized as "Machine Learning" tools.
Kubeflow and Propel are both open source tools. Kubeflow with 6.93K GitHub stars and 1K forks on GitHub appears to be more popular than Propel with 2.81K GitHub stars and 83 GitHub forks.
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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 Propel?
Propel provides a GPU-backed numpy-like infrastructure for scientific computing in JavaScript.
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What are some alternatives to Kubeflow and Propel?
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.
MLflow
MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
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.