Closing the Divide Between AI/ML Model Development and DevSecOps

Closing the Divide Between AI/ML Model Development and DevSecOps

AI and machine learning (ML) have become integral parts of our daily lives, from making restaurant reservations to online shopping. Recent data from Morgan Stanley reveals that 56% of CIOs acknowledge the direct impact of AI innovations on their investment priorities. Notably, the role of the ML Engineer is rapidly gaining prominence.

At the heart of every AI-driven application lies the model that powers it. This model is essentially another binary file that requires secure management, tracking, and deployment as an essential component of a high-quality software application. The challenge for organizations lies in the fact that model development is still a relatively nascent field, often operating in isolation and lacking integration with established software development practices.

Given the increasing prevalence of ML models in our future, DevOps and Security practitioners must address the MLOps needs of their organizations.

This involves:

  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.

Currently available in Open Beta for JFrog SaaS instances, with full hybrid support on the horizon, 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 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.

Click here to learn how SJULTRA can help you get started. You can use your existing JFrog Cloud instance or initiate a trial to follow along with our journey.