Can an open source-based workflow leveraging version control and continuous integration and deployment help streamline machine learning, like it did for software development?

MLOps, short for machine learning operations, is the equivalent of DevOps for machine learning models: Taking them from development to production, and managing their lifecycle in terms of improvements, fixes, redeployments, and so on.

Achieving MLOps nirvana is a major barrier to getting value out of machine learning and data science. Version control systems like Git and practices like continuous integration / continuous deployment (CI/CD) have helped operationalize software development.

What if those systems and practices could also be used for MLOps? Iterative.ai wants to address this question with open source projects Data Version Control and Continuous Machine Learning.

Data engineers, machine learning, and data science practitioners work with a wide range of data. They need to have a workflow and tools to support it to keep track of their artifacts and their versions, resolve issues, and collaborate across teams and systems.

Read the full article on ZDNet