JFrog and Qwak Create Secure MLOps Workflows for Accelerating the Delivery of AI Apps at Scale
New native integration empowers organizations to deliver ML applications efficiently with end-to-end software supply chain visibility, governance, and security
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JFrog and Qwak Create Secure MLOps Workflows (Graphic: Business Wire)
“Currently, data scientists and ML engineers are using a myriad of disparate tools, which are mostly disconnected from standard DevOps processes within the organization, to mature models to release. This slows MLOps processes down, compromises security, and increases the cost of building AI powered applications, ” said
Uniting JFrog Artifactory and Xray with Qwak’s ML Platform brings ML apps alongside all other software development components in a modern DevSecOps and MLOps workflow, enabling data scientists, ML engineers, Developers, Security, and DevOps teams to easily build ML apps quickly, securely, and in compliance with all regulatory guidelines. The native Artifactory integration connects JFrog’s universal ML Model registry with a centralized MLOps platform so users can easily build, train, and deploy models with greater visibility, governance, versioning, and security. Using a centralized platform for ML model deployment also allows users to focus less on infrastructure and more on their core data science tasks.
IDC research indicates that while AI/ML adoption is on the rise, the cost of implementing and training models, shortage of trained talent, and absence of solidified software development life-cycle processes for AI/ML are among the top three inhibitors to realizing the full benefits of AI/ML at scale.[1]
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Without the right infrastructure, platform and processes needed for ML operations (MLOps), it’s challenging to build, manage, and scale complex ML infrastructure, deploy models quickly, and secure them without incurring excessive costs. Companies often struggle to manage infrastructure complexity causing expensive and time-consuming authentication and security protocols between various development environments.
“AI and ML have recently transformed from being a distant future prospect to a ubiquitous reality. Building ML models is a complex and time-intensive process, which is why many data scientists are still struggling to turn their ideas into production-ready models,” said
Proof of why having secure, end-to-end MLOps processes is imperative was further confirmed by the
For a deeper look at the integration between the JFrog Platform and Qwak and how it works, read this blog or view this video. You can also register to join JFrog and Qwak for an informative webinar detailing best practices for introducing model use and development into secure software supply chain and development processes, on
Like this story? Post this on X (formerly Twitter): .@jfrog extends #MLops reach through platform integration with @Qwak_ai to unlock greater #ML #security and innovation across the #SoftwareSupplyChain. Learn more: https://jfrog.co/48sCi5O #DevOps #SDLC #MachineLearning #AI
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[1] “Machine Learning Life-Cycle Tools and Technologies,” by
View source version on businesswire.com: https://www.businesswire.com/news/home/20240228384095/en/
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