Latest Releases of Open Source Tools from Iterative.ai Extend Traditional Software Tools for Machine Learning Engineers
Open Source Projects Data Version Control (DVC) and Continuous Machine Learning (CML) extend tools like Git and CI/CD for MLOps
Iterative.ai, the MLOps company dedicated to streamlining the workflow of data scientists, announced the latest releases of Data Version Control (DVC) and Continuous Machine Learning (CML) open source projects. DVC and CML remove the need for proprietary AI Platforms (such as AWS SageMaker and Microsoft Azure ML Engineer) by extending traditional software tools like Git and CI/CD to meet the needs of ML Engineers.
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ML engineers, who work with unstructured data, need GitHub for collaboration and CI/CD systems to resolve issues between each other, between the team and production system. With a lack of adequate tools for versioning data and models to meet the needs of the ML Engineers, Iterative.ai has built open source tools, DVC and CML, on top GitHub, GitLab and BitBucket to fill this gap.
“AI Platforms are siloed and require everything to go into their own systems creating vendor lock-in,” said Dmitry Petrov, CEO and founder of Iterative.ai. “Iterative.ai allows users to stay within their application development space and effectively extend the familiar dev environments with tools to support Machine Learning Engineers and Data Scientists.”
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DVC brings agility, reproducibility, and collaboration into the existing data science workflow. DVC provides users with a Git-like interface for versioning data and models, bringing version control to machine learning and solving the challenges of reproducibility. DVC is built on top of git, allowing users to create lightweight metafiles and enabling the system to handle large files, rather than storing them in Git. It works with remote storage for large files in the cloud or on-premise network storage.
CML is an open-source library for implementing continuous integration and delivery (CI/CD) in machine learning projects. Users can automate parts of their development workflow, including model training and evaluation, comparing ML experiments across their project history, and monitoring changing datasets. CML will also auto-generate reports with metrics and plots in each Git pull request.
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