JFrog, a leading liquid software company known for the JFrog Software Supply Chain Platform, has recently launched JFrog ML, a revolutionary MLOps solution tailored to help development teams, data scientists, and ML engineers create and deploy enterprise-grade AI applications efficiently and at scale.
In response to the escalating challenges faced by enterprise AI initiatives in terms of security, scalability, and management, JFrog has established itself as the only platform worldwide capable of securely delivering machine learning technologies alongside all other application components within a single comprehensive solution. The inception of JFrog ML marks the first product integration following the acquisition of QWAK.ai in 2024, showcasing JFrog’s commitment to meeting the evolving needs of the AI landscape.
By amalgamating machine learning practices with traditional DevSecOps development processes, JFrog aims to streamline the deployment, security, and maintenance of models, thereby enhancing their performance and reliability in real-world production applications.
The launch of JFrog ML signifies JFrog’s dedication to meeting the soaring demand for scalable and secure AI application delivery. Through strategic integrations with industry-leading platforms like Hugging Face, AWS Sagemaker, MLflow developed by Databricks, and NVIDIA NIM, JFrog ML ensures a seamless and secure AI development lifecycle for organizations.
Alon Lev, VP & GM of MLOps at JFrog, emphasized the growing need for efficient controls and management of AI technologies in the face of rapid proliferation. He highlighted the unique role played by JFrog’s security researchers in discovering and remediating zero-day malicious ML models in Hugging Face. Lev explained that JFrog ML combines user-friendly deployment processes with enterprise-level trust and provenance, empowering customers to accelerate their AI initiatives with confidence.
Developing ML models and preparing them for production involves a multifaceted process that demands technical expertise and a profound understanding of software delivery. JFrog ML steps in to alleviate the complex challenges associated with this process by offering a structured framework that supports the entire organization, ensuring successful model promotion beyond the experimental stage.
Yuval Fernbach, VP & CTO of JFrog ML, highlighted the platform’s capability to streamline ML model infrastructure, from feature engineering to model deployment and monitoring. JFrog ML leverages JFrog Artifactory as the model registry and JFrog Xray for model scanning and security, providing a unified platform experience for DevOps, DevSecOps, and MLOps to propel AI development across various domains.
By treating ML models as software packages and unifying ML model management with software development, JFrog ML reduces friction and errors between stages and teams significantly. Offering AI development and deployment with full traceability, governance, and security, JFrog ML ensures a seamless and secure AI environment for organizations.
Key features of JFrog ML include:
1. A unified DevOps, DevSecOps, and MLSecOps platform that streamlines AI pipelines and securely manages models alongside other software artifacts.
2. Secured ML Models that enable AI innovation while ensuring enterprise-grade security scanning of models.
3. A single AI system of record within the JFrog Software Supply Chain Platform for managing ML models and datasets.
4. Intuitive model serving to production, simplifying model deployment and governance for data science and ML engineering teams.
5. Model training and quality monitoring with robust dataset management and feature store support.
6. Trusted ML environment offering reproducible artifact creation and rigorous security checks for AI models.
7. Support for NVIDIA NIM enterprise-grade AI Models, allowing for easy deployment of NIM-based models.
In conclusion, JFrog’s launch of JFrog ML represents a significant advancement in the MLOps space, addressing the critical need for secure, scalable, and efficient AI application delivery. With a comprehensive set of features and integrations, JFrog ML is poised to streamline AI workflow management and propel organizations towards AI innovation with confidence.