Operations | Monitoring | ITSM | DevOps | Cloud

Static delivery pipelines are becoming a bottleneck. The best CI/CD tools for Kubernetes are those that move beyond simple code builds to provide total environment orchestration and developer self-service.

Serverless platforms have revolutionized the way developers build and deploy APIs, eliminating the need to manage servers or underlying infrastructure. With serverless, you can focus entirely on your application logic and let the platform handle scaling, availability, and maintenance. Scaleway Serverless Functions is a flexible serverless platform that makes it easy to deploy lightweight APIs and background jobs in the 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.