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The Innovation vs. Control Syndrome: Unlocking Enterprise AI’s Full Potential
August 05, 2025

From optimizing supply chains to personalizing customer experiences, artificial intelligence and machine learning models are no longer statistics-based revenue initiatives; they’re foundational to modern business strategy. Organizations are pouring resources into developing and deploying AI, driven by the promise of unprecedented efficiency, insight, and competitive advantage. Yet, beneath this surging wave of innovation lies a …

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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. JFrog’s integration with NVIDIA NIM addresses these issues by applying enterprise-grade DevSecOps practices to AI development. Before exploring this solution further, let’s examine the core MLOps …

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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 …

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MLOps Your Way with the JFrog Platform
January 23, 2025

Just like in traditional software development, creating AI applications isn’t a one size fits all approach. However, many of the challenges and concerns facing AI/ML development teams share common threads – 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, …

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What is a Feature Store in ML, and Do I Need One?
January 31, 2025

In essence, a feature store is a dedicated repository where features are methodically stored and arranged, primarily for training models by data scientists and facilitating predictions in applications equipped with trained models. It stands as a pivotal gathering point, where one can formulate or modify collections of features drawn from a variety of data sources. …

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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 …

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Breaking Silos: Unifying DevOps and MLOps into a Cohesive Software Supply Chain – 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 …

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