Continuous Machine Learning
#1in Datasets & BenchmarksWhat 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 Datasets & Benchmarks category of a tech stack.
Key Features
GitFlow for data scienceAuto reports for ML experimentsNo additional services
Continuous Machine Learning Pros & Cons
Pros of Continuous Machine Learning
No pros listed yet.
Cons of Continuous Machine Learning
No cons listed yet.
Continuous Machine Learning Alternatives & Comparisons
What are some alternatives to Continuous Machine Learning?
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.
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.
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
