Code repository, JFrog, has unveiled a technology integration with Qwak, combining machine learning models with software development processes.
The integration aims to scale the development of machine learning (ML) applications.
Gal Marder, Executive Vice President of Strategy at JFrog, highlighted the existing challenges in the ML landscape: “Currently, data scientists and ML engineers grapple with disparate tools, hindering MLOps processes, compromising security, and increasing the cost of building AI-powered applications.”
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The collaboration between JFrog’s Software Supply Chain Platform – featuring Artifactory and Xray – and Qwak provides users with MLSecOps software.
This ensures ML models align with other software development processes.
This allows various teams, including data scientists, ML engineers, developers, security, and DevOps, to build ML apps quickly, securely, and in compliance with regulatory guidelines.
JFrog claims this centralised approach also enables users to focus on core data science tasks rather than infrastructure management.
According to IDC research, the rise in AI/ML adoption is hindered by the high cost of implementing and training models, a shortage of trained talent, and the absence of solidified software development life-cycle processes for AI/ML.
Jim Mercer, Program Vice President Software Development, DevOps, and DevSecOps, emphasised the complexity of building ML pipelines: “Having a single system of record that can help automate the development, providing a documented chain of provenance, and security of ML models alongside all other software components offers a compelling alternative.”
Alon Lev, CEO of Qwak, highlighted the challenges data scientists face in transforming ideas into production-ready ML models: “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.”
JFrog’s Security Research team further underscored the importance of secure MLOps processes, citing the discovery of malicious ML Models in Hugging Face, a popular AI model repository.
The findings highlighted potential threats such as code execution by threat actors, leading to data breaches and system compromise.
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By GlobalData
