Revolutionizing ML Deployment: The JFrog-Qwak Partnership

We're thrilled to announce a transformative partnership between JFrog and Qwak, set to redefine the landscape of machine learning (ML) development and deployment. This collaboration addresses the critical challenges in the ML lifecycle, propelling organizations towards achieving AI-driven innovations at scale.
Amit Ashbel
Amit Ashbel
VP Marketing at Qwak
February 28, 2024
Table of contents
Revolutionizing ML Deployment: The JFrog-Qwak Partnership

We're thrilled to announce a transformative partnership between JFrog and Qwak, set to redefine the landscape of machine learning (ML) development and deployment. This collaboration addresses the critical challenges in the ML lifecycle, propelling organizations towards achieving AI-driven innovations at scale.

A Vision for the Future

Alon Lev, CEO of Qwak, emphasizes the transformative potential of this partnership: "AI and ML have shifted from futuristic concepts to present realities. However, the complexity of building ML models remains a significant challenge. Our collaboration with JFrog aims to simplify this process, automating ML artifacts and releases in a secure manner akin to how customers manage their software supply chain with JFrog Artifactory and Xray."

Addressing the Fragmentation Challenge

Gal Marder, Executive Vice President of Strategy at JFrog, sheds light on the current state of ML operations: "Data scientists and ML engineers are bogged down by disparate tools that are largely disconnected from the standard DevOps processes. This not only slows down the MLOps processes but also compromises security and inflates the cost of building AI-powered applications." The collaboration between JFrog and Qwak introduces a complete MLSecOps solution that aligns ML models with software development processes, creating a unified platform for Engineering, MLOps, DevOps, and DevSecOps teams. This unity allows for faster, risk-minimized, and cost-effective AI application releases.

A Unified MLOps and DevSecOps Workflow

The integration brings together JFrog Artifactory and Xray with Qwak's ML Platform, positioning ML applications alongside all other software development components within a modern DevSecOps and MLOps workflow. This enables a diverse team of data scientists, ML engineers, developers, security, and DevOps professionals to build ML applications swiftly, securely, and in compliance with regulatory standards. The native integration with Artifactory offers a universal ML Model registry, facilitating greater visibility, governance, versioning, and security for ML model deployment.

Streamlining Complex ML Pipelines

The complexity of ML pipelines has long been a hurdle for teams aiming to bring models into production swiftly. This partnership targets the fragmentation and security vulnerabilities prevalent in current ML development processes, offering a unified solution that prioritizes speed, efficiency, and security.

Innovative Integration for Unmatched Value

By integrating Qwak’s ML expertise with JFrog’s robust DevOps platform, we're introducing a suite of features designed to revolutionize the ML development lifecycle:

  • Enhanced User Experience: A tailored interface and SDK specifically for AI/ML developers.
  • Effortless Integration: Existing JFrog users can seamlessly blend Qwak’s capabilities into their development cycle.
  • Comprehensive Lifecycle Management: A streamlined process for the entire ML development, from inception to deployment.
  • Advanced Data Management: Superior data governance and visibility with JFrog Artifactory, ensuring data integrity and security.
  • Synchronized Data & Artifacts: Automated processes for syncing data and artifacts, facilitating a smoother workflow.
  • Heightened Security Measures: State-of-the-art model vulnerability scanning to safeguard builds and deployments.

Overcoming Industry Inhibitors

Research by IDC highlights the rising adoption of AI/ML technologies despite challenges such as high implementation costs, talent shortages, and the lack of robust software development lifecycle processes for AI/ML. JFrog and Qwak's solution addresses these inhibitors by providing a single system of record for ML models. This system automates development, ensures a documented chain of provenance, and enhances model security and compliance, offering an optimized ML process.

The Imperative for Secure MLOps Processes

The importance of secure, end-to-end MLOps processes has been underscored by the discovery of malicious ML models in widely used repositories by the JFrog Security Research team. These vulnerabilities highlight the risks of code execution by threat actors, which can lead to data breaches and system compromises. By integrating JFrog's secure software supply chain management capabilities with Qwak's ML platform, the partnership aims to mitigate these risks, ensuring the security and integrity of ML models and applications.

Conclusion

The strategic partnership between JFrog and Qwak signifies a milestone in the integration of ML and software development processes. By providing a comprehensive and secure framework for ML model development and deployment, this collaboration is poised to streamline the delivery of AI applications at scale, offering a robust solution to the challenges that have long plagued the ML and AI development ecosystem.

Are you a JFrog customer?  Book a demo to see how you can leverage Qwak and JFrog for all your ML/AI needs.

More information about this partnership is available here

Chat with us to see the platform live and discover how we can help simplify your ML journey.

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