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Kubeflow

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numericaal

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Kubeflow vs numericaal: 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; numericaal: Machine learning for mobile & IoT made easy. numericaal automates model optimization and management so you can focus on data and training.

Kubeflow and numericaal can be primarily classified as "Machine Learning" tools.

Kubeflow is an open source tool with 7.04K GitHub stars and 1.03K GitHub forks. Here's a link to Kubeflow's open source repository on GitHub.

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Pros of Kubeflow
Pros of numericaal
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    System designer
  • 3
    Customisation
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  • 2
    Google backed
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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 numericaal?

    numericaal automates model optimization and management so you can focus on data and training.

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      What tools integrate with numericaal?
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        What are some alternatives to Kubeflow and numericaal?
        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.
        See all alternatives