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MLflow
MLflow

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numericaal
numericaal

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MLflow vs numericaal: What are the differences?

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

What is numericaal? Machine learning for mobile & IoT made easy. numericaal automates model optimization and management so you can focus on data and training.

MLflow and numericaal can be categorized 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, numericaal provides the following key features:

  • MODEL RESOURCE OPTIMIZATION - We automatically run multiple toolchains to give you the best speed, power and memory tradeoff on every model change.
  • CROSS-PLATFORM MODEL ANALYTICS - We measure on-device speed and power usage to help you evaluate and compare models across hardware platforms.
  • BOTTLENECK IDENTIFICATION - We help you pinpoint performance bottlenecks and focus your model optimization on layers that matter the most.

MLflow is an open source tool with 23 GitHub stars and 13 GitHub forks. Here's a link to MLflow's open source repository on GitHub.

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What is MLflow?

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

What is numericaal?

numericaal automates model optimization and management so you can focus on data and training.
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              What are some alternatives to MLflow and numericaal?
              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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