NLTK vs Kubeflow | What are the differences? | StackShare

Kubeflow vs NLTK

Overview

NLTK

Stacks136

Followers179

Votes0

Kubeflow

Stacks205

Followers585

Votes18

Kubeflow vs NLTK: What are the differences?

Kubeflow: 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; NLTK: It is a leading platform for building Python programs to work with human language data. It is a suite of libraries and programs for symbolic and statistical natural language processing for English written in the Python programming language.

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

Kubeflow is an open source tool with 7.23K GitHub stars and 1.08K GitHub forks. Here's a link to Kubeflow's open source repository on GitHub.

According to the StackShare community, NLTK has a broader approval, being mentioned in 15 company stacks & 17 developers stacks; compared to Kubeflow, which is listed in 3 company stacks and 8 developer stacks.

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Detailed Comparison

NLTK	Kubeflow
It is a suite of libraries and programs for symbolic and statistical natural language processing for English written in the Python programming language.	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.
Statistics
Stacks 136	Stacks 205
Followers 179	Followers 585
Votes 0	Votes 18
Pros & Cons
No community feedback yet	Pros 9 System designer 3 Customisation 3 Kfp dsl 3 Google backed 0 Azure
Integrations
No integrations available	Kubernetes Jupyter TensorFlow

What are some alternatives to NLTK, Kubeflow?

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.

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

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.

MLflow

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

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