Open Data Hub vs TensorFlow

Overview

TensorFlow

Stacks3.9K

Followers3.5K

Votes106

GitHub Stars192.3K

Forks74.9K

Open Data Hub

Stacks6

Followers22

Votes0

Open Data Hub vs TensorFlow: What are the differences?

Deployment and Management: Open Data Hub is a platform designed for deploying and managing AI/ML workloads in a scalable and efficient manner, while TensorFlow is a deep learning framework primarily used for developing and training machine learning models.
Flexibility and Extensibility: Open Data Hub provides a flexible and extensible environment for building end-to-end AI solutions, including data processing, model training, and deployment, whereas TensorFlow is more focused on providing tools and libraries for developing deep learning models.
Integrated Tools and Ecosystem: Open Data Hub comes with a comprehensive set of tools and services for data science and machine learning, including Jupyter notebooks, Apache Spark, and Apache Kafka, whereas TensorFlow has a strong ecosystem of libraries and tools, such as TensorFlow Serving and TensorFlow Lite, specifically tailored for deep learning tasks.
Collaboration and Community Support: Open Data Hub promotes collaboration and knowledge sharing among data science teams by providing a shared platform for experimentation and model development, whereas TensorFlow has a large and active community that contributes to its development and provides support for users through forums, documentation, and tutorials.
Supported Use Cases: Open Data Hub caters to a wide range of use cases in AI/ML, including predictive analytics, natural language processing, and computer vision, while TensorFlow is more specialized for deep learning tasks like image recognition, speech recognition, and natural language processing.
Scalability and Performance: Open Data Hub offers scalable infrastructure for running AI workloads on clusters of nodes, ensuring high performance and resource utilization, whereas TensorFlow provides optimized algorithms and execution systems to achieve high performance on GPUs and TPUs for training deep neural networks efficiently.

In Summary, Open Data Hub and TensorFlow differ in their focus, with Open Data Hub providing a platform for end-to-end AI solution development and deployment, while TensorFlow is primarily a deep learning framework with a strong emphasis on model development and training.

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Advice on TensorFlow, Open Data Hub

Adithya

Student at PES UNIVERSITY

May 11, 2020

Needs advice

I have just started learning some basic machine learning concepts. So which of the following frameworks is better to use: Keras / TensorFlow/PyTorch. I have prior knowledge in python(and even pandas), java, js and C. It would be nice if something could point out the advantages of one over the other especially in terms of resources, documentation and flexibility. Also, could someone tell me where to find the right resources or tutorials for the above frameworks? Thanks in advance, hope you are doing well!!

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Detailed Comparison

TensorFlow	Open Data Hub
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.	It is an open source project that provides open source AI tools for running large and distributed AI workloads on OpenShift Container Platform. Currently, It provides open source tools for data storage, distributed AI and Machine Learning (ML) workflows and a Notebook development environment.
-	Open source project; AI tools for running large and distributed AI workloads on OpenShift Container Platform; Tools for data storage, distributed AI and Machine Learning
Statistics
GitHub Stars 192.3K	GitHub Stars -
GitHub Forks 74.9K	GitHub Forks -
Stacks 3.9K	Stacks 6
Followers 3.5K	Followers 22
Votes 106	Votes 0
Pros & Cons
Pros 32 High Performance 19 Connect Research and Production 16 Deep Flexibility 12 Auto-Differentiation 11 True Portability Cons 9 Hard 6 Hard to debug 2 Documentation not very helpful	No community feedback yet
Integrations
JavaScript	No integrations available

What are some alternatives to TensorFlow, Open Data Hub?

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/

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.

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.

Related Comparisons

Open Data Hub vs TensorFlow: What are the differences?

Deployment and Management: Open Data Hub is a platform designed for deploying and managing AI/ML workloads in a scalable and efficient manner, while TensorFlow is a deep learning framework primarily used for developing and training machine learning models.
Flexibility and Extensibility: Open Data Hub provides a flexible and extensible environment for building end-to-end AI solutions, including data processing, model training, and deployment, whereas TensorFlow is more focused on providing tools and libraries for developing deep learning models.
Integrated Tools and Ecosystem: Open Data Hub comes with a comprehensive set of tools and services for data science and machine learning, including Jupyter notebooks, Apache Spark, and Apache Kafka, whereas TensorFlow has a strong ecosystem of libraries and tools, such as TensorFlow Serving and TensorFlow Lite, specifically tailored for deep learning tasks.
Collaboration and Community Support: Open Data Hub promotes collaboration and knowledge sharing among data science teams by providing a shared platform for experimentation and model development, whereas TensorFlow has a large and active community that contributes to its development and provides support for users through forums, documentation, and tutorials.
Supported Use Cases: Open Data Hub caters to a wide range of use cases in AI/ML, including predictive analytics, natural language processing, and computer vision, while TensorFlow is more specialized for deep learning tasks like image recognition, speech recognition, and natural language processing.
Scalability and Performance: Open Data Hub offers scalable infrastructure for running AI workloads on clusters of nodes, ensuring high performance and resource utilization, whereas TensorFlow provides optimized algorithms and execution systems to achieve high performance on GPUs and TPUs for training deep neural networks efficiently.

Open Data Hub vs TensorFlow

Overview