Introducing JFrog ML: Unifying DevOps, DevSecOps and MLOps for a Secure Software Supply Chain Platform

It’s no secret that AI and machine learning have moved out of the lab and into the critical path of software development. As organizations accelerate their adoption of GenAI and ML models into business applications, there’s a growing need to treat these models with the same rigor, processes, and security that are standard for software code.

Recognizing the convergence of AI/ML and traditional software, JFrog acquired Qwak, a pioneering startup in the MLOps space, to bridge this critical gap. We are excited to announce that Qwak will now be known as JFrog ML, as we take the next step to unify DevOps, DevSecOps, and MLOps under a single, secure software supply chain platform.

The Vision: ML Models as First-Class Citizens in the Software Pipeline

The transition from traditional software to GenAI and ML models is not just an evolution; it’s a revolution. However, despite the power of AI/ML to transform the value applications can deliver to users, ML model development often exists in a siloed workflow, disconnected from the standard software supply chain. This separation often leads to inefficiencies, security vulnerabilities, and slowed innovation.

JFrog ML brings the vision that ML models must be treated as first-class citizens within the software pipeline to life. Just as code packages, the ML ecosystem needs a solution that offers similar capabilities for models, ensuring they are versioned, stored securely, and easily integrated into production environments. For Data Scientists and ML Engineers, JFrog ML not only makes it easy to build and deploy AI/ML services, but also ensures that their work is seamlessly made available to developers across the org. 

Why Now: The Urgency of Unifying Software and ML Pipelines

The urgency to adopt a solution like JFrog ML stems from the accelerating pace of ML adoption across industries. As organizations deploy more ML models, they face challenges that traditional software pipelines have already solved: managing dependencies, ensuring security, and automating deployment.

However, GenAI and ML models bring unique challenges. They require handling large datasets, ensuring reproducibility of results, and managing the complexities of model versioning. If left unchecked, these challenges can lead to bottlenecks, security risks and degraded performance.

JFrog ML and the JFrog Platform address these challenges head-on to combine the best practices of DevOps with the specialized needs of MLOps. This unified approach not only streamlines the development and deployment of ML models but also ensures they are subject to the same security measures that protect the rest of your software supply chain.

The Power of Integration: Trust and Control

Delivering trust has always been at the heart of JFrog’s offerings, and JFrog ML is no exception. By integrating ML models into the secure software supply chain, organizations can rest assured that their models are protected from threats throughout their lifecycle. Teams also gain confidence knowing the origin and usage of AI/ML components throughout their applications. 

With JFrog ML, models are automatically stored in a secure repository, scanned for vulnerabilities, and governed by the same policies that protect your code. This seamless integration means that as your ML models evolve, their security and governance is maintained without additional overhead or manual intervention.

Looking Ahead: A Future of Unified AI/ML and Software Pipelines

As we launch JFrog ML, we are excited to help organizations embrace the future of software development. By treating AI/ML models as integral components of the software pipeline, JFrog ML empowers DevOps and MLOps leaders to innovate faster, deploy more securely, and maintain the agility needed to stay ahead in a competitive landscape.

The convergence of DevOps and MLOps is no longer a distant vision—it’s a necessity. JFrog ML is here to make that vision a reality, ensuring that your software and ML pipelines are ready for whatever challenges lie ahead.

Ready to take your ML models to the next level? Explore JFrog ML today and see how you can unify your software and ML pipelines under a single, secure platform.