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  1. Stackups
  2. AI
  3. Development & Training Tools
  4. Machine Learning Tools
  5. Caffe vs Continuous Machine Learning

Caffe vs Continuous Machine Learning

OverviewComparisonAlternatives

Overview

Caffe
Caffe
Stacks66
Followers73
Votes0
GitHub Stars34.7K
Forks18.6K
Continuous Machine Learning
Continuous Machine Learning
Stacks21
Followers37
Votes0
GitHub Stars4.1K
Forks346

Caffe vs Continuous Machine Learning: What are the differences?

Caffe: A deep learning framework. It is a deep learning framework made with expression, speed, and modularity in mind; Continuous Machine Learning: CI/CD for Machine Learning Projects. Continuous Machine Learning (CML) is an open-source library for implementing continuous integration & delivery (CI/CD) in machine learning projects. Use it to automate parts of your development workflow, including model training and evaluation, comparing ML experiments across your project history, and monitoring changing datasets.

Caffe and Continuous Machine Learning can be categorized as "Machine Learning" tools.

Some of the features offered by Caffe are:

  • Extensible code
  • Speed
  • Community

On the other hand, Continuous Machine Learning provides the following key features:

  • GitFlow for data science
  • Auto reports for ML experiments
  • No additional services

Caffe is an open source tool with 30.6K GitHub stars and 18.4K GitHub forks. Here's a link to Caffe's open source repository on GitHub.

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Detailed Comparison

Caffe
Caffe
Continuous Machine Learning
Continuous Machine Learning

It is a deep learning framework made with expression, speed, and modularity in mind.

Continuous Machine Learning (CML) is an open-source library for implementing continuous integration & delivery (CI/CD) in machine learning projects. Use it to automate parts of your development workflow, including model training and evaluation, comparing ML experiments across your project history, and monitoring changing datasets.

Extensible code; Speed; Community;
GitFlow for data science; Auto reports for ML experiments; No additional services
Statistics
GitHub Stars
34.7K
GitHub Stars
4.1K
GitHub Forks
18.6K
GitHub Forks
346
Stacks
66
Stacks
21
Followers
73
Followers
37
Votes
0
Votes
0
Integrations
TensorFlow
TensorFlow
Keras
Keras
Amazon SageMaker
Amazon SageMaker
Pythia
Pythia
GitHub
GitHub
Git
Git
GitLab
GitLab
Google Cloud Platform
Google Cloud Platform
DVC
DVC

What are some alternatives to Caffe, Continuous Machine Learning?

TensorFlow

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

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

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

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Kubeflow

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.

TensorFlow.js

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

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

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.

MLflow

MLflow

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

H2O

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.

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