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Kubeflow

123
410
+ 1
13
Apache Spark

2.1K
2.3K
+ 1
131
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Kubeflow vs Apache Spark: What are the differences?

Developers describe Kubeflow as "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. On the other hand, Apache Spark is detailed as "Fast and general engine for large-scale data processing". 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.

Kubeflow and Apache Spark are primarily classified as "Machine Learning" and "Big Data" tools respectively.

Kubeflow and Apache Spark are both open source tools. Apache Spark with 22.5K GitHub stars and 19.4K forks on GitHub appears to be more popular than Kubeflow with 7.04K GitHub stars and 1.03K GitHub forks.

Pros of Kubeflow
Pros of Apache Spark
  • 5
    System designer
  • 3
    Customisation
  • 3
    Kfp dsl
  • 2
    Google backed
  • 58
    Open-source
  • 47
    Fast and Flexible
  • 7
    One platform for every big data problem
  • 6
    Easy to install and to use
  • 6
    Great for distributed SQL like applications
  • 3
    Works well for most Datascience usecases
  • 2
    Machine learning libratimery, Streaming in real
  • 2
    In memory Computation
  • 0
    Interactive Query

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Cons of Kubeflow
Cons of Apache Spark
    Be the first to leave a con
    • 2
      Speed

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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 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.

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    What companies use Kubeflow?
    What companies use Apache Spark?

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    What tools integrate with Kubeflow?
    What tools integrate with Apache Spark?

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    What are some alternatives to Kubeflow and Apache Spark?
    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.
    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.
    Argo
    Argo is an open source container-native workflow engine for getting work done on Kubernetes. Argo is implemented as a Kubernetes CRD (Custom Resource Definition).
    See all alternatives
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