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MLflow

138
406
+ 1
8
Pipelines

27
67
+ 1
0
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MLflow vs Pipelines: What are the differences?

Developers describe MLflow as "An open source machine learning platform". MLflow is an open source platform for managing the end-to-end machine learning lifecycle. On the other hand, Pipelines is detailed as "Machine Learning Pipelines for Kubeflow". Kubeflow is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable. Kubeflow pipelines are reusable end-to-end ML workflows built using the Kubeflow Pipelines SDK.

MLflow and Pipelines can be primarily classified as "Machine Learning" tools.

MLflow and Pipelines are both open source tools. Pipelines with 944 GitHub stars and 247 forks on GitHub appears to be more popular than MLflow with 23 GitHub stars and 13 GitHub forks.

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    What is MLflow?

    MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

    What is Pipelines?

    Kubeflow is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable. Kubeflow pipelines are reusable end-to-end ML workflows built using the Kubeflow Pipelines SDK.

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    What tools integrate with MLflow?
    What tools integrate with Pipelines?
    What are some alternatives to MLflow and Pipelines?
    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.
    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.
    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.
    DVC
    It is an open-source Version Control System for data science and machine learning projects. It is designed to handle large files, data sets, machine learning models, and metrics as well as code.
    Seldon
    Seldon is an Open Predictive Platform that currently allows recommendations to be generated based on structured historical data. It has a variety of algorithms to produce these recommendations and can report a variety of statistics.
    See all alternatives