Iterative Tools School now open and free to the community; teaches data scientists how to take advantage of popular open source machine learning tools
Iterative, the MLOps company dedicated to streamlining the workflow of data scientists and machine learning (ML) engineers, today announced Iterative Tools School, a free online course for data scientists to learn how to use Iterative tools, including DVC, CML, and Studio.
“The Iterative community has long been asking for an online course to learn Iterative’s GitOps approach to solving many of the challenges faced by data scientists and machine learning engineers,” said Jeny De Figueiredo, community manager of Iterative. “This course and the new learning platform meets the needs of data scientists looking for solutions to productionize their work and provides us a platform to create ongoing content to help educate the community on these best practices.”
Iterative Tools School teaches skills with a practical approach: Learning is broken down into digestible modules with additional practice lessons. In this course, students will learn the best practices from software engineering and apply them to machine learning projects and build their own customized platform without the heavy lifting of a homegrown solution by leveraging shared strategies and knowledge learned in this course.
“The goal of the course is to help data scientists understand our approach and is working already!”
“Using Git and other development tools as a foundation along with our tools, this course breaks down the wall between machine learning modeling and software development so that these two worlds can be mutually productive,” said Dmitry Petrov, founder and CEO of Iterative. “The goal of the course is to help data scientists understand our approach and is working already!”
“A data scientist without knowledge of software engineering practices gains so much value out of this course,” said Kristoffer Johansson, AI product owner of Region Västra Götaland, an Iterative customer.
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.
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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.