Kubeflow vs PredictionIO vs PyTorch

Kubeflow

100
312
+ 1
8
PredictionIO

63
87
+ 1
5
PyTorch

631
670
+ 1
18
Advice on Kubeflow, PredictionIO, and PyTorch
Adithya Shetty
Student at PES UNIVERSITY · | 5 upvotes · 40.3K views
Needs advice
on
TensorFlow
PyTorch
and
Keras

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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      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 PredictionIO?

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

      What is 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.
      What companies use Kubeflow?
      What companies use PredictionIO?
      What companies use PyTorch?

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      What tools integrate with Kubeflow?
      What tools integrate with PredictionIO?
      What tools integrate with PyTorch?
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        What are some alternatives to Kubeflow, PredictionIO, and PyTorch?
        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.
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