Software Alternatives, Accelerators & Startups

Open Data Hub VS TFlearn

Compare Open Data Hub VS TFlearn and see what are their differences

Open Data Hub logo Open Data Hub

OpenDataHub

TFlearn logo TFlearn

TFlearn is a modular and transparent deep learning library built on top of Tensorflow.
  • Open Data Hub Landing page
    Landing page //
    2023-06-01
Not present

Open Data Hub videos

Open Data Hub Introduction

More videos:

  • Review - Fraud Detection Using Open Data Hub on Openshift
  • Review - Installing Open Data Hub on OpenShift 4.1

TFlearn videos

Face Recognition using Deep Learning | Convolutional-Neural-Network | TensorFlow | TfLearn

Category Popularity

0-100% (relative to Open Data Hub and TFlearn)
Data Science And Machine Learning
OCR
0 0%
100% 100
Data Dashboard
27 27%
73% 73
Data Science Tools
100 100%
0% 0

User comments

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Social recommendations and mentions

Based on our record, Open Data Hub should be more popular than TFlearn. It has been mentiond 3 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Open Data Hub mentions (3)

  • job scheduling for scientific computing on k8s?
    Perhaps have a look at OpenDataHub. While geared for Openshift, see if they solved some of your concerns. Source: about 1 year ago
  • Elyra 2.2: R support, updated CLI, and more
    A common approach is to deploy JupyterHub on Kubernetes and configure it for Elyra, like it is done in Open Data Hub on the Red Hat OpenShift Container platform. - Source: dev.to / over 3 years ago
  • Automate your machine learning workflow tasks using Elyra and Apache Airflow
    If you are interested in running pipelines on Apache Airflow on the Red Hat OpenShift Container Platform, take a look at Open Data Hub. Open Data Hub is an open source project (just like Elyra) that should include everything you need to start running machine learning workloads in a Kubernetes environment. - Source: dev.to / over 3 years ago

TFlearn mentions (2)

  • Beginner Friendly Resources to Master Artificial Intelligence and Machine Learning with Python (2022)
    TFLearn – Deep learning library featuring a higher-level API for TensorFlow. - Source: dev.to / almost 2 years ago
  • Base ball
    Both the teams in a game are given their individual ID values and are made into vectors. Relevant data like the home and away team, home runs, RBI’s, and walk’s are all taken into account and passed through layers. There’s no need to reinvent the wheel here, there's a multitude of libraries that enable a coder to implement machine learning theories efficiently. In this case we will be using a library called... - Source: dev.to / about 3 years ago

What are some alternatives?

When comparing Open Data Hub and TFlearn, you can also consider the following products

IBM Watson Studio - Learn more about Watson Studio. Increase productivity by giving your team a single environment to work with the best of open source and IBM software, to build and deploy an AI solution.

Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.

C3 AI Suite - The C3 AI Suite uses a model-driven architecture to accelerate delivery and reduce the complexities of developing enterprise-scale AI applications.

Clarifai - The World's AI

TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.

DeepPy - DeepPy is a MIT licensed deep learning framework that tries to add a touch of zen to deep learning as it allows for Pythonic programming.