As Software as a Service (SaaS) solutions evolve, organizations face increasing pressure to ensure uninterrupted service delivery. One of the most significant threats to SaaS Service delivery and operational continuity is the presence of known and unknown Single Points of Failure (SPOFs). As a SaaS organization, the team at JFrog deeply understands the risks of โฆ
Every day, your developers are pushing software. Some of that software will make it to production, but many of those incremental builds will not. While you shouldnโt remove those incremental builds and old release versions haphazardly, if left unchecked, they can clog up your software repositories as well as the workflows and systems they serve. โฆ
Managing and securing the software supply chain is crucial for trusted releases, but as any tech organization knows, it also presents significant challenges. With over 15 years of experience and a dedicated security research team, we at JFrog understand these threats. In a rapidly evolving post-AI world, DevSecOps teams are struggling to keep pace with โฆ
In a LinkedIn Live panel discussion hosted by Melissa McKay, Head of Developer Relations at JFrog, thought leaders from NVIDIA, GitHub, and JFrog came together to discuss the transformative power of AI in modern software development. This session delved into three key topics: the integration of AI in the software development lifecycle (SDLC), strategies for โฆ
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 โฆ
In this blog series, we will explore the importance of merging DevOps best practices with MLOps to bridge this gap, enhance an enterpriseโs 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, weโll โฆ
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 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
JFrog has long supported standards widely used by developers, including OCI container images. We started with our OCI-compliant Docker registry, then followed up with dedicated JFrog Artifactory OCI repositories. In our continued commitment to developer freedom of choice, weโre excited to take another leap forward. JFrog is now fully conformant to OCI v1.1. Source: OCI 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
