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Kubeflow
Kubeflow

17
29
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0
ML Kit
ML Kit

117
153
+ 1
0
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Kubeflow vs ML Kit: 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, ML Kit is detailed as "Machine learning for mobile developers (by Google)". ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.

Kubeflow and ML Kit belong to "Machine Learning Tools" category of the tech stack.

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.

- No public GitHub repository available -

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 ML Kit?

ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.
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            What are some alternatives to Kubeflow and ML Kit?
            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.
            scikit-learn
            scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
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