Rigorous audit helps ensure the safety and privacy of customers’ data
Iterative, the MLOps company dedicated to streamlining the workflow of data scientists and machine learning (ML) engineers, announced it has earned SOC 2 Type 1 compliance, reinforcing Iterative’s ability and commitment to safeguarding the privacy and security of customer data.
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“Iterative takes data security and integrity very seriously”
SOC 2 compliance is part of the American Institute of CPAs’ Service Organization Control reporting platform. Its intent is to ensure the safety and privacy of your customers’ data. It outlines five trust service principles of security, availability, processing integrity, confidentiality, and privacy of customer data as a framework for safeguarding data.
“Iterative takes data security and integrity very seriously,” said Ivan Shcheklein, co-founder and CTO of Iterative. “Through SOC 2 Compliance we can demonstrate this commitment to our customers in a very tangible way. With customers in sensitive sectors like banking and healthcare, data security and integrity have always been a priority for us since day one. This is true for both open source and commercial tools. Achieving SOC2 compliance by an independent auditor formalizes this and proves our commitment to customers in a very tangible way.”
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 experiment reproducibility. DVC is built to easily track and manage large data files as part of the software development lifecycle by creating lightweight, Git-tracked metafiles, and building data-aware experiments and pipelines. This enables ML engineers to seamlessly integrate their cloud storage infrastructure with their source control system, capitalizing on both, without compromising on data provenance.
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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 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 to build a model registry in hours rather than days.
Together, DVC, CML, and MLEM provide ML Engineers a number of features and benefits that support data provenance, machine learning model management and automation. DVC, CML, and MLEM 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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