JFrog has always prioritized universality, ensuring software development teams have true freedom of choice. Core to the JFrog Platform, JFrog Artifactory is the world’s most versatile artifact manager, natively supporting nearly 40 package types. After taking in valuable feedback from the developer community, we’re thrilled to discuss how we’re further expanding our universe with the support of Hex packages.

In today’s hybrid infrastructure landscape, migrating applications to the cloud unlocks significant financial and technological benefits. Whether internal or external, these applications require robust, efficient infrastructure. Cloud providers like Amazon Web Services (AWS), Google Cloud, and Microsoft Azure offer frameworks to help organizations build better systems.

We here at JFrog have long advocated for promoting – never rebuilding – release candidates as they advance across the stages of your SDLC. For many JFrog customers, that meant using JFrog’s “Build Promotion” capabilities. Now you can level up your CI/CD game with promotions using Release Lifecycle Management (RLM)! In this article we’ll show you why promotions with RLM are more simple, secure, and scalable than our legacy build promotion API.

The speed of today’s software development lifecycle is only getting faster. However, the complexity of today’s pipelines make it hard to track and manage the processes software releases must go through. With increasing regulatory pressures, ensuring and proving your software has gone through the necessary quality controls is no longer nice to have – it has become a necessity.

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.

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 to integration isn’t without its twists and turns.

In this blog series, we will explore the importance of merging DevOps best practices with MLOps to bridge this gap, enhance an enterprise’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.