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MLflow
MLflow

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ScalaNLP
ScalaNLP

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MLflow vs ScalaNLP: 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, ScalaNLP is detailed as "A suite of machine learning and numerical computing libraries". ScalaNLP is a suite of machine learning and numerical computing libraries.

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

Some of the features offered by MLflow are:

  • Track experiments to record and compare parameters and results
  • Package ML code in a reusable, reproducible form in order to share with other data scientists or transfer to production
  • Manage and deploy models from a variety of ML libraries to a variety of model serving and inference platforms

On the other hand, ScalaNLP provides the following key features:

  • ScalaNLP is the umbrella project for several libraries:
  • Breeze is a set of libraries for machine learning and numerical computing
  • Epic is a high-performance statistical parser and structured prediction library

MLflow and ScalaNLP are both open source tools. ScalaNLP with 2.91K GitHub stars and 674 forks on GitHub appears to be more popular than MLflow with 23 GitHub stars and 13 GitHub forks.

What is MLflow?

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

What is ScalaNLP?

ScalaNLP is a suite of machine learning and numerical computing libraries.
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            What tools integrate with MLflow?
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            What are some alternatives to MLflow and ScalaNLP?
            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.
            scikit-learn
            scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
            Keras
            Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
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