What is Continuous Machine Learning?
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.
Continuous Machine Learning is a tool in the Machine Learning Tools category of a tech stack.
Continuous Machine Learning is an open source tool with 2.7K GitHub stars and 195 GitHub forks. Here’s a link to Continuous Machine Learning's open source repository on GitHub
Who uses Continuous Machine Learning?
18 developers on StackShare have stated that they use Continuous Machine Learning.
Continuous Machine Learning Integrations
GitHub, Git, GitLab, Google Cloud Platform, and DVC are some of the popular tools that integrate with Continuous Machine Learning. Here's a list of all 5 tools that integrate with Continuous Machine Learning.
Continuous Machine Learning's Features
- GitFlow for data science
- Auto reports for ML experiments
- No additional services
Continuous Machine Learning Alternatives & Comparisons
What are some alternatives to Continuous Machine Learning?
See all alternatives
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.
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.
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.