Source: thenewstack.io
Iterative.ai: Git-Based Machine Learning Tools for Data EngineersCategory: Database, Data, Microsoft, github, gitlab, bitbucket, artificial-intelligence
There needed to be tools built on top of platforms, they needed to be open source and that machine learning engineers had particular needs not being met.
Its two products DVC (Data Version Control) and CML (Continuous Machine Learning) aim to bring engineering practices to data science and machine learning. In the ever-growing ecosystem of DataOps enterprise software vendors, including DVC joins the likes of TerminusDB, Dolt and Pachyderm with the aim to bring a Git-like experience to data science, but Petrov says the focus of DVC is narrow — versioning data and ML models.
That’s where DVC and CML library comes in.DVC offers a way to track changes in data, source code and ML models together to provide a single history of a project.
Versions of the data and models are stored as Git commits, enabling users to create snapshots, restore previous versions, reproduce experiments, and more They can manage experiments with Git tags/branches and metrics tracking.
Its two products DVC (Data Version Control) and CML (Continuous Machine Learning) aim to bring engineering practices to data science and machine learning. In the ever-growing ecosystem of DataOps enterprise software vendors, including DVC joins the likes of TerminusDB, Dolt and Pachyderm with the aim to bring a Git-like experience to data science, but Petrov says the focus of DVC is narrow — versioning data and ML models.
That’s where DVC and CML library comes in.DVC offers a way to track changes in data, source code and ML models together to provide a single history of a project.
Versions of the data and models are stored as Git commits, enabling users to create snapshots, restore previous versions, reproduce experiments, and more They can manage experiments with Git tags/branches and metrics tracking.
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