Continuous Machine Learning vs Paperspace

Paperspace vs Continuous Machine Learning: What are the differences?

Paperspace: The way to access and manage limitless computing power in the cloud. It is a high-performance cloud computing and ML development platform for building, training and deploying machine learning models. Tens of thousands of individuals, startups and enterprises use it to iterate faster and collaborate on intelligent, real-time prediction engines; 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.

Paperspace belongs to "Machine Learning as a Service" category of the tech stack, while Continuous Machine Learning can be primarily classified under "Machine Learning Tools".

Some of the features offered by Paperspace are:

• Intelligent alert
• Two-factor authentication
• Share drives

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

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