Closing the Divide Between AI/ML Model Development and DevSecOps

Closing the Divide Between AI/ML Model Development and DevSecOps
  • By Steve Chambers
  • in Software Supply Chain

Do your AI/ML Models sit inside or outside of your secure software supply chain?

AI/ML models, which underpin AI capabilities, are essentially large binary files requiring secure management, tracking, and deployment alongside traditional software components: they need to be managed just like, if not inside, your secure software supply chain.

However, model development often operates in isolation from established software practices. 

This article explains how SJULTRA can help you bridge the software and AI/MLOps words for a more complete software supply chain operation.

Table of Contents

5 steps to secure your Ai/ML models and operations

The steps to secure AI/ML models is much the same as software, and the same supply chain techniques can be applied. 

At SJULTRA, we find that the trust in models is not something our clients’ take lightly, so the absence of models in the secure supply chain would be a concern. 

Here’s what we look for:

  1. Establishing local repositories for proprietary and internally enhanced ML and GenAI models, with scalability to handle large binary files.
  2. Creating remote repositories to proxy public model hubs, enabling organizations to implement new AI-enabled features quickly.
  3. Ensuring a single source of truth that automates model development, management, and security.
  4. Exercising control and governance over which the ML/data science teams adopt models in anticipation of forthcoming regulations.
  5. The ability to manage models alongside other binaries, seamlessly incorporating ML models into software releases.

Introducing JFrog's Machine Learning Model Management

JFrog’s Machine Learning Model Management simplifies the process for DevOps and Security teams to meet their organization’s MLOps requirements.

This solution seamlessly integrates into the workflows of ML Engineers and Data Scientists, allowing organizations to apply their existing practices and policies to ML model development. Furthermore, it extends the secure software supply chain.

Manage ML Models As
Part of Your Secure
Software Supply Chain

 
Create a single system of record for ML models that
brings ML/AI development in line with your existing
SDLC. ML Model Management allows
you to store first party models and proxy Hugging
Face with scanning for security and license issues.

 

ML Model Management enables JFrog users to utilize Artifactory for managing proprietary models and proxy Hugging Face, a leading model hub, for third-party models integration. Within Artifactory, users can incorporate models into immutable Release Bundles for development towards release and distribution.

In addition, by utilizing JFrog Xray’s industry-leading ML security capabilities, organizations can identify and block malicious or non-compliant models.

 

The Advantages of AI/ML Model Management

ML Model Management provides organizations with a unified platform for managing all software binaries, thereby introducing DevOps best practices to ML development. It ensures the integrity and security of ML models while leveraging an existing solution that organizations already have in place.

SJULTRA can help you get started through our Managed ML Models service.

You can use your existing JFrog Cloud instance or initiate a trial to follow along with our journey.

Ask us anything about AI/ML Model Management as part of your secure software supply chain.

Unify ML, Dev, Sec, And Ops with SJULTRAAdd Your Heading Text Here

SJULTRA can help you bring the management of AI/ML models into your secure software supply chain.

See how SJULTRA can help your team create a single system of record for ML models that brings ML/AI development in line with your existing SDLC.

  • Bring in pypi, CRAN, Conan, Conda, and other software components for a unified view of the software you’re building and releasing.
  • Apply the same best practices you use for package management to model management.
  • Unlike Git and point solutions, leveraging JFrog for model management provides the best in performance, scalability, and optimized handling of large binary files.
  • Store models alongside their required data, files, and packages to easily bundle them into a secure Release Bundle for maturation towards release distribution.

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