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  1. Stackups
  2. AI
  3. Development & Training Tools
  4. Machine Learning Tools
  5. FeatHub vs Kubeflow

FeatHub vs Kubeflow

OverviewComparisonAlternatives

Overview

Kubeflow
Kubeflow
Stacks205
Followers585
Votes18
FeatHub
FeatHub
Stacks0
Followers1
Votes0
GitHub Stars340
Forks57

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Detailed Comparison

Kubeflow
Kubeflow
FeatHub
FeatHub

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.

It is a feature store that facilitates feature development and deployment to reduce duplication of data engineering efforts, simplify feature management, and facilitate feature development-to-deployment iteration

-
Define features via table joins with point-in-time correctness; Define over window aggregation features; Define sliding window aggregation features; Define features via built-in functions; Define feature via Python UDF
Statistics
GitHub Stars
-
GitHub Stars
340
GitHub Forks
-
GitHub Forks
57
Stacks
205
Stacks
0
Followers
585
Followers
1
Votes
18
Votes
0
Pros & Cons
Pros
  • 9
    System designer
  • 3
    Customisation
  • 3
    Kfp dsl
  • 3
    Google backed
  • 0
    Azure
No community feedback yet
Integrations
Kubernetes
Kubernetes
Jupyter
Jupyter
TensorFlow
TensorFlow
Python
Python
Apache Flink
Apache Flink

What are some alternatives to Kubeflow, FeatHub?

TensorFlow

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

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

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.

Keras

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

TensorFlow.js

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

Polyaxon

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

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.

MLflow

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

PredictionIO

PredictionIO

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.

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