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Accelerating Enterprise AI Development: A Guide to the JFrog-NVIDIA NIM Integration
March 04, 2025

Enterprises are racing to integrate AI into applications, yet transitioning from prototype to production remains challenging. Managing ML models efficiently while ensuring security and governance is a critical challenge. JFrogโ€™s integration with NVIDIA NIM addresses these issues by applying enterprise-grade DevSecOps practices to AI development. Before exploring this solution further, letโ€™s examine the core MLOps โ€ฆ

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JFrog and Hugging Face Join Forces to Expose Malicious ML Models
March 04, 2025

ML operations, data scientists, and developers currently face critical security challenges on multiple fronts. First, staying up to date with evolving attack techniques requires constant vigilance and security know-how, which can only be achieved by a dedicated security team. Second, existing ML model scanning engines suffer from a staggering rate of false positives. When a โ€ฆ

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MLOps Your Way with the JFrog Platform
January 23, 2025

Just like in traditional software development, creating AI applications isnโ€™t a one size fits all approach. However, many of the challenges and concerns facing AI/ML development teams share common threads โ€“ difficulties getting models to production, tangled infrastructure, data quality, security issues, and so on. Regardless of how you build it, to accelerate production-ready AI, โ€ฆ

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Breaking Silos: Unifying DevOps and MLOps into a Cohesive Software Supply Chain โ€“ Part 3
January 15, 2025

The synergy between DevOps and MLOps is more crucial now than ever. However, merging these two paradigms into a coherent software supply chain poses a unique set of challenges that can leave teams feeling overwhelmed. From the complexities of managing model dependencies to adapting conventional CI/CD tools for advanced machine learning (ML) workflows, the path โ€ฆ

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Breaking Silos: Unifying DevOps and MLOps into a Cohesive Software Supply Chain โ€“ Part 2
December 19, 2024

In this blog series, we will explore the importance of merging DevOps best practices with MLOps to bridge this gap, enhance an enterpriseโ€™s competitive edge, and improve decision-making through data-driven insights. Part one discussed the challenges of separate DevOps and MLOps pipelines and outlined a case for integration. In this second of three blogs, weโ€™ll โ€ฆ

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Feature Store Benefits: The Advantages of Feature Stores in Machine Learning Development
October 22, 2024

Feature stores are rapidly growing in popularity as organizations look to improve their machine learning productivity and operations (MLOps). With the advancements in MLOps, feature stores are becoming an essential component of the machine learning infrastructure, helping organizations to improve the performance and ability to explain their models, and accelerate the integration of new models โ€ฆ

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Breaking Silos: Unifying DevOps and MLOps into a Cohesive Software Supply Chain โ€“ Part 1
December 04, 2024

As businesses realized the potential of artificial intelligence (AI), the race began to incorporate machine learning operations (MLOps) into their commercial strategies. But the integration of machine learning (ML) into the real world proved challenging, and the vast gap between development and deployment was made clear. In fact, research from Gartner tells us 85% of โ€ฆ

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Machine Learning Bug Bonanza โ€“ Exploiting ML Services
November 04, 2024

JFrogโ€™s security research team continuously monitors open-source software registries, proactively identifying and addressing potential malware and vulnerability threats to foster a secure and reliable ecosystem for open-source software development and deployment. In our previous research on MLOps we noted the immaturity of the Machine Learning (ML) field often results in a higher amount of discovered โ€ฆ

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swampUP Recap: โ€œEveryOpsโ€ is Trending as a Software Development Requirement
October 23, 2024

swampUP 2024, the annual JFrog DevOps Conference, was unique in itโ€™s addressing not only more familiar DevOps and DevSecOps issues, but adding specific operational challenges, stemming from the explosive growth of GenAI and the resulting need for specialized capabilities for handling AI models and datasets, while supporting new personae such as AI/ML engineers, data scientists โ€ฆ

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