Operations | Monitoring | ITSM | DevOps | Cloud

In my previous posts, we discussed the where and the how of managing your ML assets. We showed you how JFrog Artifactory acts as a powerful, universal model registry (the “where”) and how the FrogML SDK serves as the gateway to get your models and metadata into it (the “how”). Now, let’s talk about the why.

Amazon Elastic Kubernetes Service (Amazon EKS) is a fully managed, compliant Kubernetes service that simplifies running, managing, and scaling containerized applications. EKS automatically handles the availability and scalability of the Kubernetes control plane, allowing teams of any size or skill level to focus on building and deploying production-ready applications across diverse environments, including AWS, on-premises, and at the edge.

When we first introduced the JFrog AI Catalog, it was our mission to provide the industry with a single system of record for governing the complex landscape of internal, open-source, and external commercial AI models. This foundational step was critical for enterprises to move from uncontrolled innovation to delivering AI with trust and confidence. However, the AI landscape is ever-evolving. The challenge for today’s enterprise is already evolving beyond simply managing a library of known models.

A fundamental shift in software development is already here. Gartner predicts that by 2028, 75% of enterprise software engineers will use AI code assistants – a massive leap from less than 10% in early 2023. While this AI-driven speed creates a competitive advantage, it also opens a dangerous new front in the battle for software supply chain security.

AI has created a paradigm shift in software development. AI-native development teams – from small startups to enterprises like Goldman Sachs and Google – are adopting agentic development tools like Cursor and Copilot to increase the speed of code generation to a pace we’ve never seen before. But with all this new code comes a big challenge: how do you manage all these potential new releases and get the right ones deployed?

In my previous blog, we demonstrated how the FrogML SDK streamlines the process of integrating custom-built or publicly sourced models from your IDE into JFrog Artifactory. Now that your models are securely stored, versioned, and managed, the natural next question arises: “Ok, so you have some models in JFrog Artifactory, now what?” This is where the real power of the JFrog Platform comes into play.

Data migrations have long been a significant source of anxiety for businesses and IT teams alike. The thought of moving critical databases often conjures images of prolonged downtime, service interruptions, and the ever-present risk of data loss. Indeed, statistics show that “90% of businesses experience unexpected downtime during database migrations, leading to significant revenue loss and customer dissatisfaction”.

Today’s software organizations can’t make tradeoffs between speed and trust – you need both to succeed. But juggling them is tough. Moving too fast can lead to security vulnerabilities and compliance issues, while moving too slow means your competitors beat you to market. This tension creates friction that slows down every release, a problem that is rooted in your software pipeline.

Data-driven decisions are critical. And to support high-stakes decision-making – from fraud detection in credit card transactions to demand forecasting in retail – organizations are increasingly relying on complex models. According to McKinsey, 78% of organizations report using AI in at least one business function, highlighting just how embedded AI and ML models have become in operational and strategic decision-making.

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 growing tension: the Innovation vs. Control Syndrome.