Growth reflects continued desire for tooling that enables best software engineering practices for machine learning
Iterative, the MLOps company dedicated to streamlining the workflow of data scientists and machine learning (ML) engineers, announced that it has seen steady growth in the first half of the year, Including explosive adoption of the DVC extension for VS Code and Iterative Tools School enrollment.
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“We remain committed to our mission to deliver the best developer experience for machine learning teams by creating an ecosystem of open, modular ML tools.”
Announced in June, the DVC Extension for Visual Studio Code allows users of all technical backgrounds to create, compare, visualize, and reproduce machine learning experiments. Through Git and Iterative’s DVC, the extension makes experiments easily reproducible, unlike traditional experiment tracking tools that just stream metrics. Since its launch, the extension has been installed more than 8,500 times and has five stars on the Visual Studio Marketplace.
Iterative has also seen growth in enrollment for the Iterative Tools School since being announced in March. A free online course for data scientists to learn how to use Iterative tools, including DVC, CML, and Iterative Studio. Enrollment has kept a steady 30% monthly growth with over 1,800 students currently enrolled in the program.
“DVC users have increased 50% since the start of 2022 and the steady growth of both the VS Code extension and student enrollment validates that we are on the right track when it comes to creating tooling to bridge the gap between data science and software engineering teams,” said Dmitry Petrov, co-founder and CEO at Iterative. “We remain committed to our mission to deliver the best developer experience for machine learning teams by creating an ecosystem of open, modular ML tools.”
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Iterative’s 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, which can’t be stored in Git. It works with remote storage for large files in the cloud.
Also from Iterative, 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.
Additionally, Iterative’s Machine Learning Engineering Management (MLEM) provides a modular nature that fits into any organization’s software development workflows based on Git and CI/CD, without engineers having to transition to a separate machine learning deployment and registry tool. This allows teams to use a similar process across both ML models and applications for deployment, eliminating duplication in processes and code. Teams are then able build a model registry in hours rather than days.
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 that encompasses all of its open-source Unix-philosophy tools into one collaboration service called Iterative Studio.
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