JFrog Launches JFrog ML: Revolutionizing AI Development with Enhanced Security and MLOps Integration

JFrog Becomes an AI System of Record, Launches JFrog ML " Industry's First End-to-End DevOps, DevSecOps & MLOps Platform for Trusted AI Delivery By Investing.com

JFrog ML Drives MLOps practices coupled with AI Security - Unifying Developer, Data Science & Operations Teams with Enterprise-wide Automation & Control of AI-powered Software (ETR:SOWGn) Delivery SUNNYVALE, Calif. & NEW YORK--(BUSINESS WIRE)--JFrog Ltd (Nasdaq: FROG), the Liquid Software company and creators of the JFrog Software Supply Chain Platform, today released JFrog ML, a revolutionary MLOps solution as part of the JFrog Platform designed to enable development teams, data scientists and ML engineers to quickly develop and deploy enterprise-ready AI applications at scale. As enterprise AI initiatives increasingly face security, scalability and management challenges, JFrog is now the only platform in the world that drives the secure delivery of machine learning technologies alongside all other application components in a single solution. JFrog ML is the first addition to the platform that resulted from QWAK.ai acquisition in 2024. By seamlessly uniting machine learning (ML) practices with traditional DevSecOps development processes, organizations can help ensure their models are seamlessly deployed, secured, and maintained, which is expected to enhance model performance and dependability in real-world, production applications. The delivery of JFrog ML is an outcropping of JFrog's commitment to address the demand for more scalable, secure AI application delivery, including integrations with Hugging Face, AWS Sagemaker, MLflow (developed by Databricks), and NVIDIA (NASDAQ:NVDA) NIM. "As the demand for AI-powered applications continues to grow rapidly, so do the concerns around the ability to control and manage this new domain on all fronts " from MLOps to ML security. In fact, our own team of security researchers were the first to find and help remediate new, zero-day malicious ML models in Hugging Face," said Alon Lev, VP & GM, MLOps, JFrog. "JFrog ML combines superior, straightforward and hassle-free user experience for bringing models to production, combined with the level of trust and provenance enterprises expect from JFrog, allowing customers to accelerate their AI initiatives with confidence." Developing ML models and making them production-ready is an extremely complex process, today demanding a blend of technical expertise and a deep understanding of software delivery. Models require careful planning and testing to ensure reliability and efficiency in a live environment. Additionally, Data Scientists building models don't work in isolation"they need data engineers to structure and prepare data, software engineers to deploy models as microservices, and DevSecOps teams to ensure smooth and secure integration into production. JFrog ML helps overcome these often-crippling challenges with a structured framework designed to support the entire organization and ensure that models successfully get promoted out of experimental stages. "Building and maintaining robust ML workflows requires a complex infrastructure, from feature engineering to model deployment and monitoring. JFrog ML is designed to enable these capabilities by utilizing JFrog Artifactory as the model registry of choice and JFrog Xray for scanning and securing ML models, making it possible to enhance user efficiency by providing a unified platform experience for DevOps, DevSecOps, and MLOps," said Yuval Fernbach, VP & CTO, JFrog ML. "As AI evolves, organizations can leverage JFrog ML to continuously adapt their infrastructure to support everything from traditional ML models to cutting-edge GenAI applications." By treating ML models as software packages from the start of development and converging ML model management and software development into a single source of truth, the friction and errors between stages and teams can be significantly reduced. JFrog ML delivers AI development and deployment with full traceability, governance and security. Key features include: For more information on JFrog ML read this blog or visit https://jfrog.com/jfrog-ml. You can also connect with JFrog ML experts at the inaugural MLOps Days community event, taking place March 4, 2025 in New York City, or during NVIDIA GTC, the premiere AI conference, taking place March 17 - 21, 2025 in San Jose, California. Learn more, register, and book a meeting or hands-on demo here. Like this story? Post this on X (formerly Twitter): .@jfrog doubles-down on #MLOps with JFrog ML, bridging the gap between ML and #DevSecOps teams. Learn more: https://bit.ly/41BnHVm #DevOps #developers #JFrogML #machinelearning About JFrog JFrog Ltd. (Nasdaq: FROG) is on a mission to securely power the world with Liquid Software, streamlining application delivery from developer to device. Our JFrog Software Supply Chain Platform enables organizations to build, manage, and securely distribute software, ensuring applications are traceable and tamper-proof. Built for advancing the world of AI, our platform aligns ML models with development processes, providing a unified source of truth for Engineering, MLOps, DevOps, and DevSecOps teams. This integration allows faster AI application releases with minimized risks and costs. Additionally, our platform features robust security to identify and remediate threats. Available as both self-hosted and SaaS services, JFrog is trusted by millions, including many Fortune 100 companies, to facilitate secure digital transformation. Discover more at jfrog.com and follow us on X: @jfrog. Cautionary Note About Forward-Looking Statements This press release contains forward-looking statements, as that term is defined under the U.S. federal securities laws, including, but not limited to, statements regarding expected enhancements in model performance and dependability, anticipated acceleration of AI initiatives, anticipated reduction of friction and errors in the development process, and expected improvements in security and simplification of model governance. These forward-looking statements are based on our current assumptions, expectations, and beliefs and are subject to substantial risks, uncertainties, assumptions and changes in circumstances that may cause JFrog's actual results, performance or achievements to differ materially from those expressed or implied in any forward-looking statement. There are a significant number of factors that could cause actual results, performance or achievements to differ materially from statements made in this press release, including but not limited to risks detailed in our filings with the Securities and Exchange Commission, including in our annual report on Form 10-K for the year ended December 31, 2024, our quarterly reports on Form 10-Q, and other filings and reports that we may file from time to time with the Securities and Exchange Commission. Forward-looking statements represent our beliefs and assumptions only as of the date of this press release. We disclaim any obligation to update forward-looking statements except as required by law. View source version on businesswire.com: https://www.businesswire.com/news/home/20250304885900/en/ Media Contact: Siobhan Lyons, Sr. MarComm Manager, JFrog, siobhanL@jfrog.com Investor Contact: Jeff Schreiner, VP of Investor Relations, jeffS@jfrog.com

JFrog and Hugging Face Team to Improve Machine Learning Security and Transparency for Developers By Investing.com

New integration significantly improves the quality and trustworthiness of open-source ML Models, resulting in safer, more responsible AI for everyone SUNNYVALE, Calif. & NEW YORK--(BUSINESS WIRE)--JFrog Ltd (Nasdaq: FROG), the Liquid Software (ETR:SOWGn) company and creators of the JFrog Software Supply Chain Platform, today announced it is partnering with Hugging Face, host of the world's largest repository of public machine learning (ML) models " the Hugging Face Hub " designed to achieve more robust security scans and analysis for every ML model in their library. The new integration is designed to provide higher levels of trust for scanning results by prominently displaying a JFrog Certified checkmark, so developers, data scientists, and ML Engineers know which models are safer to use. As ML models become integral to critical business applications, ensuring these models are secure is crucial for preventing breaches, data leaks, and decision-making errors, said Asaf Karas, CTO of JFrog Security. We've been working with Hugging Face since 2023 to help securely bring ML Models to production. We also found intentionally malicious models in Hugging Face in early 2024, which prompted us to dedicate more of our security experts to help scan and assess the well-being of all Hugging Face models to ensure they are safe for use in AI application development. Machine learning (ML) introduces a new set of supply chain assets, such as models and datasets, which not only come with their own security challenges but also increase an organization's attack surface. These newer areas of the ML supply chain may allow nefarious actors to achieve remote code execution to infiltrate and spread malicious code inside an organization through ML Models. This could potentially grant access to critical internal systems and pave the way for large-scale data breaches or even corporate espionage, impacting not just individual users but potentially entire organizations across the globe. Ensuring ML Model Integrity with JFrog Advanced Security JFrog Xray and JFrog Advanced Security " key components of the JFrog Software Supply Chain Platform " are designed to scan AI/ML model artifacts for threats at every stage of their lifecycle. These threats include model serialization attacks, known CVEs, backdoors, and more. Now Hugging Face will utilize JFrog Advanced Security scans in its Hugging Face Hub, allowing each model contained within the platform to be scanned in advance of being downloaded for use. The results of each scan will be prominently displayed for all users to see. This new advanced security integration between Hugging Face and JFrog differs from existing ML model scanners due to JFrog's malicious code decompilation and deep data flow analysis. While existing solutions simply check for automatically-executed code embedded in a model, JFrog's model scanner uses an enhanced approach to extract and analyze the embedded code which eliminates more than 96% of false positives produced by other scanners on current Hugging Face models. In addition, JFrog's enhanced analysis highlighted 25 models as zero-day malicious in nature. These are machine learning models hosted in Hugging Face which were not identified as malicious by any other scanner available for Hugging Face based on our evaluation. Surveys have found that while over 80% of enterprises are using or experimenting with AI applications, more than 90% feel they are unprepared for AI security challenges. Additionally, cybersecurity agencies from the U.S., the U.K., and Canada have jointly issued warnings, advising businesses to carefully scan any pre-trained models for harmful code. For a long time, AI was a researcher's field, and the security practices were quite basic, but as the popularity and widespread use of AI grows, so do the number of potentially bad actors who may want to target the AI community in general and our platform more specifically, said Julien Chaumond, CTO, Hugging Face. As the leading collaboration platform for AI models, we're delighted to deepen our partnership with JFrog to implement high-quality scanning capabilities for our AI/ML models and deliver greater peace of mind for developers looking to create the next generation of AI-powered applications. For a deeper look at how ML Model scanning of Hugging Face is being performed using the JFrog Platform, read this blog or learn more about JFrog's Hugging Face integration, scanning malicious AI models, and model threat categories. You can also learn more about how JFrog and other AI industry players are contributing to AI/ML security at the inaugural MLOps Days community event, taking place March 4, 2025 in New York City, or during NVIDIA (NASDAQ:NVDA) GTC, the premiere AI conference, taking place March 17 - 21, 2025 in San Jose, California. Learn more, register, or book a meeting for a demo here. We welcome the community to send feedback on this integration directly to JFrog's security research team at research@jfrog.com. Like this story? Post this on X (formerly Twitter): @JFrog and @huggingface unite to provide integrated security scanning tools in the Hugging Face platform, helping users detect malicious code before downloading any #ML models. Learn more: https://jfrog.co/41kYaOT #MLOps #AI #softwaresupplychain #security #DevSecOps About JFrog JFrog Ltd. (Nasdaq: FROG) is on a mission to create a world of software delivered without friction from developer to device. Driven by a Liquid Software vision, the JFrog Software Supply Chain Platform is a single system of record that powers organizations to build, manage, and distribute software quickly and securely, ensuring it is available, traceable, and tamper-proof. The integrated security features also help identify, protect, and remediate against threats and vulnerabilities. JFrog's hybrid, universal, multi-cloud platform is available as both self-hosted and SaaS services across major cloud service providers. Millions of users and 7K+ customers worldwide, including a majority of the Fortune 100, depend on JFrog solutions to securely embrace digital transformation. Once you leap forward, you won't go back! Learn more at jfrog.com and follow us on X: @jfrog Cautionary Note About Forward-Looking Statements This press release contains forward-looking statements, as that term is defined under the U.S. federal securities laws, including, but not limited to, statements regarding our expectations regarding increased levels of safety and security of the integrated product and anticipated increased trust of users related to the model scanner. These forward-looking statements are based on our current assumptions, expectations, and beliefs and are subject to substantial risks, uncertainties, assumptions and changes in circumstances that may cause JFrog's actual results, performance or achievements to differ materially from those expressed or implied in any forward-looking statement. There are a significant number of factors that could cause actual results, performance or achievements to differ materially from statements made in this press release, including but not limited to risks detailed in our filings with the Securities and Exchange Commission, including in our annual report on Form 10-K for the year ended December 31, 2024, our quarterly reports on Form 10-Q, and other filings and reports that we may file from time to time with the Securities and Exchange Commission. Forward-looking statements represent our beliefs and assumptions only as of the date of this press release. We disclaim any obligation to update forward-looking statements except as required by law. View source version on businesswire.com: https://www.businesswire.com/news/home/20250304244002/en/ Media Contact: Siobhan Lyons, Sr. Manager, Global Communications, siobhanL@jfrog.com Investor Contact: Jeff Schreiner, VP of Investor Relations, jeffS@jfrog.com