Kubeflow vs MLflow vs numericaal
Overview
Detailed Comparison
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. | MLflow is an open source platform for managing the end-to-end machine learning lifecycle. | numericaal automates model optimization and management so you can focus on data and training. |
| - | 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 | 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. |
Statistics | ||
GitHub Stars - | GitHub Stars 22.8K | GitHub Stars - |
GitHub Forks - | GitHub Forks 5.0K | GitHub Forks - |
Stacks 205 | Stacks 222 | Stacks 0 |
Followers 585 | Followers 524 | Followers 18 |
Votes 18 | Votes 9 | Votes 0 |
Pros & Cons | ||
Pros
| Pros
| No community feedback yet |
Integrations | ||
| No integrations available | No integrations available | |
What are some alternatives to Kubeflow, MLflow, numericaal?
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.
PyTorch
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
TensorFlow.js
Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API
Polyaxon
An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.
Streamlit
It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.
H2O
H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.
PredictionIO
PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.
Gluon
A new open source deep learning interface which allows developers to more easily and quickly build machine learning models, without compromising performance. Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components.
