Every day development teams around the world release new software. But what happens to prior releases that are no longer in production? Most organizations save them, typically due to internal policies, external regulations, or simply the fear of losing data. Organizations typically take varied approaches to retaining their prior releases. Some use a dedicated repository โฆ
swampUP 2024, the annual JFrog DevOps Conference, was unique in itโs 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 โฆ
At swampUP 2024 in Austin just a few days ago, we explored the EveryOps Matters approach with the crowd of developers, driven by a consolidated view from their companiesโ boardrooms and 2024 CIO surveys. The message was clear: โEveryOpsโ isnโt just a strategy or tech trend โ itโs a fundamental, ongoing mindset shift that must โฆ
The AI revolution is transforming enterprises faster than you can say, โsudo apt-get install skynet.โ According to McKinsey, 65% of organizations now regularly use generative AI, nearly doubling from last year. However, as developers rush to integrate AI into their products, the shift from AI proof-of-concept to production can feel like trying to assemble flat-box โฆ
Todayโs Hurdles to Efficient Software Development Picture this: Youโve just settled in at home after a long day, ready to relax, when suddenly your phone buzzes. Itโs a notification about a failed build in your latest project. Your heart sinks. Your mind starts racing to connect the dotsโฆ What went wrong? Where is it broken? โฆ
According to Gartner, almost two-thirds of U.S. businesses were directly impacted by a software supply chain attack. So itโs not a question of whether to secure your software supply chain, but rather what is the most effective and efficient way to provide end-to-end security during all phases of the software development lifecycle (SDLC). The โฆ
Developers simply want to write code without interruption, while operations wish to build as fast as possible and deploy without restrictions. On the other hand, security professionals want to protect every step of the software supply chain from any potential security threats and vulnerabilities. In software development, every piece of code can potentially introduce vulnerabilities โฆ
Just a few years back, developing AI/ML (Machine Learning) models was a secluded endeavor, primarily undertaken by small teams of developers and data scientists away from public scrutiny. However, with the surge in GenAI/LLMs, open-source models, and ML development tools, thereโs been a significant democratization of model creation, with more developers and organizations engaging in โฆ
An organizationโs software supply chain includes all the elements involved in developing and distributing software, such as components, tools, processes, and dependencies. Each link in this important chain presents the potential for security threats. Recent research conducted by Gartner shows a major increase in attacks targeting code, tools, open-source components, and development processes, particularly in โฆ
Modern AI applications are having a dramatic impact on our industry, but there are still certain hurdles when it comes to bringing ML models to production. The process of building ML models is so complex and time-intensive that many data scientists still struggle to turn concepts into production-ready models. Bridging the gap between MLOps and โฆ
