Growth reflects Iterative’s mission to streamline the workflow of data scientists and machine learning engineers
Iterative, the MLOps company dedicated to streamlining the workflow of data scientists and machine learning (ML) engineers, reached a number of important milestones in 2021. Highlights include introducing the Data Version Control (DVC) and Continuous Machine Learning (CML) open source projects followed by the addition of Experiment Versioning in DVC.
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Iterative was founded in 2018 and in less than three years, its tools have had more than 8 million sessions and are rapidly growing, with more than 12,000 stars on GitHub between CML and DVC. DVC users grew by almost 95% in 2021 with over 3000 monthly users. Iterative now has more than 300 contributors across the different tools.
“Iterative’s tools have been critical in helping our machine learning team grow and unlock their productivity,” said Benjamin Jones, head of ML at DeGould. “Before using DVC, we struggled to share data and pipelines were steps written in READMEs. Since adopting DVC and CML, we’ve been able to easily collaborate and share data and experiments across team members to improve productivity and to show progress. Iterative has made life easier with tools that work with our existing tech stack, instead of running Bash scripts to cobble everything together. And sending progress reports in emails with spreadsheets is a thing of the past!”
In 2021, Iterative saw significant company growth as the headcount increased by 150%. Joining the team include Oded Messer as director of Engineering and Ken Thom as director of Operations. Messer brings more than 10 years of experience as a software engineer where he most recently worked as platform group manager at Iguazio following five years as software engineer at Intel. Thom brings 30 years of experience in the software industry where he was most recently managing director at SOMAcentral following various product positions at companies including SocialMedia.com, Unboggle, oDesk, Vazu Inc., Inktomi, E*Trade, and Apple.
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“We are excited to welcome Oded and Ken to the team,” said Dmitry Petrov, co-founder and CEO of Iterative. “We look forward to continued expansion of our team to build the best tools for machine learning engineers.”
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, creating lightweight metafiles and enabling the system to handle large files, which can’t be stored in Git. It works with remote storage for large files in the cloud.
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.
Together, CML and DVC provide ML engineers a number of features and benefits that support data provenance, machine learning model management and automation. DVC and CML are open-source tools available for free. Iterative also provides a commercial offering of a collaboration service DVC Studio. Iterative is building additional open-source tools to complement the ML engineering workflow, and also provides a commercial offering of a collaboration service Iterative Studio.
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