JFrog Becomes an AI System of Record, Launches JFrog ML – Industry's First End-to-End DevOps, DevSecOps & MLOps Platform for Trusted AI Delivery
JFrog (NASDAQ: FROG) has launched JFrog ML, a revolutionary MLOps solution integrated into the JFrog Platform, marking the first product release following the QWAK.ai acquisition in 2024. This solution unifies DevOps, DevSecOps, and MLOps practices, enabling teams to develop and deploy enterprise-ready AI applications at scale.
The platform offers unique features including enterprise-grade model security scanning, unified model management alongside software artifacts, and simplified model deployment processes. JFrog ML integrates with major AI platforms including Hugging Face, AWS Sagemaker, MLflow, and NVIDIA NIM.
Key capabilities include:
- A unified DevOps and MLSecOps platform
- Secured ML model scanning for malicious or vulnerable models
- Single AI system of record for ML models and datasets
- Intuitive model serving to production
- Complete dataset management and monitoring
- Support for NVIDIA NIM enterprise-grade AI models
JFrog (NASDAQ: FROG) ha lanciato JFrog ML, una soluzione MLOps rivoluzionaria integrata nella JFrog Platform, segnando il primo rilascio di prodotto dopo l'acquisizione di QWAK.ai nel 2024. Questa soluzione unifica le pratiche di DevOps, DevSecOps e MLOps, consentendo ai team di sviluppare e distribuire applicazioni AI pronte per l'impresa su larga scala.
La piattaforma offre funzionalità uniche, tra cui scansione della sicurezza dei modelli di livello enterprise, gestione unificata dei modelli insieme agli artefatti software e processi semplificati di distribuzione dei modelli. JFrog ML si integra con le principali piattaforme AI, tra cui Hugging Face, AWS Sagemaker, MLflow e NVIDIA NIM.
Le capacità chiave includono:
- Una piattaforma unificata di DevOps e MLSecOps
- Scansione sicura dei modelli ML per modelli dannosi o vulnerabili
- Un sistema di registrazione AI unico per modelli ML e dataset
- Servizio di modelli intuitivo in produzione
- Gestione e monitoraggio completi dei dataset
- Supporto per modelli AI di livello enterprise NVIDIA NIM
JFrog (NASDAQ: FROG) ha lanzado JFrog ML, una solución MLOps revolucionaria integrada en la JFrog Platform, marcando el primer lanzamiento de producto tras la adquisición de QWAK.ai en 2024. Esta solución unifica las prácticas de DevOps, DevSecOps y MLOps, permitiendo a los equipos desarrollar y desplegar aplicaciones de IA listas para la empresa a gran escala.
La plataforma ofrece características únicas, incluyendo escaneo de seguridad de modelos de nivel empresarial, gestión unificada de modelos junto a artefactos de software y procesos simplificados de despliegue de modelos. JFrog ML se integra con las principales plataformas de IA, incluyendo Hugging Face, AWS Sagemaker, MLflow y NVIDIA NIM.
Las capacidades clave incluyen:
- Una plataforma unificada de DevOps y MLSecOps
- Escaneo seguro de modelos ML para modelos maliciosos o vulnerables
- Un sistema único de registro de IA para modelos ML y conjuntos de datos
- Servicio de modelos intuitivo en producción
- Gestión y monitoreo completos de conjuntos de datos
- Soporte para modelos de IA de nivel empresarial NVIDIA NIM
JFrog (NASDAQ: FROG)는 JFrog ML을 출시했습니다. 이는 JFrog 플랫폼에 통합된 혁신적인 MLOps 솔루션으로, 2024년 QWAK.ai 인수 후 첫 번째 제품 출시를 의미합니다. 이 솔루션은 DevOps, DevSecOps 및 MLOps 관행을 통합하여 팀이 대규모로 기업 준비가 완료된 AI 애플리케이션을 개발 및 배포할 수 있도록 합니다.
플랫폼은 기업 수준의 모델 보안 스캔, 소프트웨어 아티팩트와 함께하는 통합 모델 관리, 간소화된 모델 배포 프로세스 등 독특한 기능을 제공합니다. JFrog ML은 Hugging Face, AWS Sagemaker, MLflow 및 NVIDIA NIM과 같은 주요 AI 플랫폼과 통합됩니다.
주요 기능은 다음과 같습니다:
- 통합된 DevOps 및 MLSecOps 플랫폼
- 악성 또는 취약한 모델에 대한 보안 ML 모델 스캔
- ML 모델 및 데이터 세트를 위한 단일 AI 기록 시스템
- 생산을 위한 직관적인 모델 제공
- 완전한 데이터 세트 관리 및 모니터링
- NVIDIA NIM 기업 수준의 AI 모델 지원
JFrog (NASDAQ: FROG) a lancé JFrog ML, une solution MLOps révolutionnaire intégrée à la JFrog Platform, marquant le premier lancement de produit suite à l'acquisition de QWAK.ai en 2024. Cette solution unifie les pratiques DevOps, DevSecOps et MLOps, permettant aux équipes de développer et de déployer des applications d'IA prêtes pour l'entreprise à grande échelle.
La plateforme offre des fonctionnalités uniques, y compris un scan de sécurité des modèles de niveau entreprise, une gestion unifiée des modèles aux côtés des artefacts logiciels, et des processus de déploiement des modèles simplifiés. JFrog ML s'intègre avec les principales plateformes d'IA, y compris Hugging Face, AWS Sagemaker, MLflow et NVIDIA NIM.
Les capacités clés incluent:
- Une plateforme unifiée de DevOps et MLSecOps
- Scan sécurisé des modèles ML pour détecter les modèles malveillants ou vulnérables
- Un système d'enregistrement AI unique pour les modèles ML et les ensembles de données
- Service de modèles intuitif pour la production
- Gestion et surveillance complètes des ensembles de données
- Support pour les modèles AI de niveau entreprise NVIDIA NIM
JFrog (NASDAQ: FROG) hat JFrog ML eingeführt, eine revolutionäre MLOps-Lösung, die in die JFrog-Plattform integriert ist und die erste Produktveröffentlichung nach der Übernahme von QWAK.ai im Jahr 2024 markiert. Diese Lösung vereint DevOps-, DevSecOps- und MLOps-Praktiken, sodass Teams KI-Anwendungen für Unternehmen in großem Maßstab entwickeln und bereitstellen können.
Die Plattform bietet einzigartige Funktionen, darunter Sicherheitsüberprüfung von Modellen auf Unternehmensniveau, einheitliches Modellmanagement zusammen mit Software-Artefakten und vereinfachte Modellbereitstellungsprozesse. JFrog ML integriert sich mit wichtigen KI-Plattformen wie Hugging Face, AWS Sagemaker, MLflow und NVIDIA NIM.
Wichtige Funktionen sind:
- Eine einheitliche DevOps- und MLSecOps-Plattform
- Gesicherte ML-Modellüberprüfung auf schädliche oder anfällige Modelle
- Ein einziges AI-System zur Aufzeichnung von ML-Modellen und Datensätzen
- Intuitive Bereitstellung von Modellen in der Produktion
- Umfassendes Datenmanagement und -überwachung
- Unterstützung für NVIDIA NIM AI-Modelle auf Unternehmensniveau
- First product launch post QWAK.ai acquisition shows quick integration
- Only platform offering enterprise-grade ML model security scanning
- Strategic partnerships with major AI platforms (Hugging Face, AWS, NVIDIA)
- Unified platform reducing operational complexity and team friction
- Complex implementation requiring multiple technical expertise
- High dependency on technical teams coordination (data scientists, engineers, DevSecOps)
Insights
JFrog's launch of JFrog ML represents a significant strategic expansion that positions the company at the critical intersection of DevOps and artificial intelligence. Building on their 2024 QWAK.ai acquisition, JFrog has created what appears to be the first comprehensive platform unifying traditional software delivery with machine learning operations.
This move directly addresses a major pain point in enterprise AI adoption - the fragmentation between development teams, data scientists, and operations. By creating a unified system of record for both traditional software artifacts and ML models, JFrog is solving real organizational friction that has AI implementation success rates.
The timing is particularly advantageous as enterprises move from AI experimentation to production deployment at scale, where security, governance and operational concerns become paramount. JFrog's existing strengths in artifact management and software supply chain security provide natural differentiation versus pure-play MLOps startups lacking enterprise security credentials.
Integration with industry-standard ML tools like Hugging Face, AWS SageMaker, and NVIDIA NIM demonstrates pragmatic platform thinking rather than attempting to replace existing investments. By leveraging their existing customer base of development teams and extending to data science departments, JFrog can expand wallet share while addressing legitimate organizational challenges.
The real value proposition centers on reducing the implementation gap between model development and production deployment - precisely where most enterprise AI initiatives currently falter. If JFrog can successfully execute on this vision, they stand to capture significant value in what Gartner identifies as one of technology's fastest-growing segments.
JFrog's entry into MLOps with JFrog ML addresses several critical technical barriers preventing enterprise AI adoption. Most notably, they've tackled the fundamental disconnect between how data scientists develop models and how engineering teams deploy software.
The platform's approach to treating ML models as software packages from the development outset is technically sound. This creates reproducible artifacts that can undergo the same rigorous security scanning and quality checks as other software components - essential for compliance-focused enterprises in regulated industries.
Their model security scanning capability is particularly noteworthy. As the article mentions, JFrog's security researchers discovered zero-day vulnerabilities in Hugging Face models, demonstrating real domain expertise rather than merely adding "AI" labels to existing products. This security-first approach aligns with enterprise priorities as organizations recognize the unique attack vectors introduced by ML models.
The integration with NVIDIA NIM models provides a pathway for enterprises to leverage cutting-edge foundation models with simplified deployment, potentially accelerating adoption of more sophisticated AI capabilities beyond what internal teams could develop independently.
From an architectural perspective, using JFrog Artifactory as the model registry creates a familiar interface for DevOps teams while extending its capabilities to handle ML-specific requirements. This approach reduces the learning curve for operations teams already familiar with JFrog's platform while providing the specialized capabilities data scientists require.
The technical challenge now lies in execution - particularly in areas like model monitoring, where detecting drift and ensuring performance requires specialized capabilities beyond traditional application monitoring.
JFrog ML Drives MLOps practices coupled with AI Security - Unifying Developer, Data Science & Operations Teams with Enterprise-wide Automation & Control of AI-powered Software Delivery
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 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:
- A unified DevOps, DevSecOps and MLSecOps platform: JFrog ML as part of the JFrog Platform provides a holistic view of the entire software supply chain, from traditional software packages to LLMs and GenAI, streamlining AI pipelines and ensuring models are securely managed alongside other software artifacts.
- Secured ML Models: Enables AI innovation while keeping companies secure with the only platform providing off-the-shelf, enterprise-grade model security scanning of malicious or vulnerable models generated by your company - or those brought in from open source.
- A single AI system of record: Part of the JFrog Software Supply Chain Platform, JFrog ML manages ML models and datasets alongside other building blocks such as containers and Python packages, creating one place to enforce customizable security and compliance policies throughout the AI development process.
- Intuitive model serving to production: JFrog ML helps supercharge AI initiatives with simplified model development and deployment processes, helping data science and ML engineering teams accelerate model serving in production while dramatically improving security and simplifying model governance, rollback, and redeployment.
- Model training and quality monitoring: Complete dataset management and feature store support.
- Trusted ML environment: JFrog ML creates a reproducible artifact of every model built with the JFrog Platform, allowing for security scans and automated quality checks to ensure your models have been as rigorously vetted as your other software components.
- Support for NVIDIA NIM enterprise-grade AI Models: JFrog ML catalog will also include serving NIM-based models as part of its model library, allowing for one-click deployment.
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
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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
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
Source: JFrog Ltd.
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