Operations | Monitoring | ITSM | DevOps | Cloud

We are witnessing a fundamental transformation in how software is built. The industry has moved beyond the experimental phase of Machine Learning Operations and entered a complex new reality: the era of the AI Software Supply Chain. The adoption metrics confirm this shift is irreversible. Google reports that 90% of tech workers are now using AI as part of their daily work. Similarly, McKinsey data reveals that 88% of organizations use AI in at least one business function.

Fine-tuning Large Language Models (LLMs) on private, domain-specific data can unlock significant value for your specific use case. When done correctly, you can create AI apps that understand your organization’s unique context. These apps can speak your brand’s voice and deliver remarkably accurate results that general models cannot match. However, finetuning is not always the right solution. Many teams rush into this complex technique without exploring simpler alternatives first.

Manually configuring DNS records for staging and production environments is a common pain point for developers and DevOps teams. As your organization grows and you manage more applications across different services, keeping DNS records up-to-date and error-free becomes increasingly challenging and time-consuming. Mistakes in DNS setup can lead to downtime, broken environments, or confusing deployments, especially when juggling multiple teams or microservices.

Shipping application code, especially for LLM-based applications, can be a stressful and complex task. These applications demand intricate model management, careful resource allocation, and manual handling of dependency conflicts. Traditionally, preparing such applications for deployment involves integration tests, containerization, and updating image registries: all time-consuming manual steps. This is where an automated CI pipeline becomes invaluable.

Sigstore’s Fulcio certificate authority now recognizes CircleCI’s OIDC tokens. This means you can sign container images and other artifacts directly from your CircleCI pipelines—without managing long-lived signing keys.

Every year is a big year for Bitbucket, but in 2025, we delivered transformative changes that cap off years of work to make Bitbucket Cloud the secure, scalable, cloud-first standard for large engineering teams around the world. Today, 15M developers build on Bitbucket, including all of Atlassian’s 10,000-strong engineering organization, and Bitbucket Pipelines runs more than 1 billion build minutes per month.

Stop managing complex YAML and plugin hell. Compare the top 10 CI/CD platforms for DevOps, from Jenkins to Qovery, and learn how to build high-velocity, automated pipelines on your own cloud.

Recently, we introduced autonomous validation as a new approach to CI/CD that brings adaptive, context-aware intelligence into the delivery pipeline. As AI increases both the volume and reach of code changes, teams are seeing more failures, longer queues, and rising maintenance costs. Traditional pipelines simply weren’t built for this level of velocity or variability.

Development teams depend on microservices to build, deploy, and scale features independently. Microservices have become the backbone of modern, scalable applications. Scaleway’s managed Kubernetes service (Kubernetes Kapsule) offers a powerful, cost-effective platform for running containerized workloads in the cloud. It’s a great fit for startups and solo engineers who want to focus on shipping features, not managing infrastructure.

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.