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Direct API-Database Coupling vs. Multi-Layered Architectures

API-database coupling vs. traditional multi-layered architectures: what’s the difference and why does it matter? The main difference between direct API-database coupling and multi-layered architectures is that the former allows the API to interact directly with the database, minimizing latency and complexity, while the latter uses multiple layers to separate concerns.

How ClearML Helps Teams Get More out of Slurm

It is a fairly recent trend for companies to amass GPU firepower to build their own AI computing infrastructure and support the growing number of compute requests. Many recent AI tools now enable data scientists to work on data, run experiments, and train models seamlessly with the ability to submit their jobs and monitor their progress. However, for many organizations with mature supercomputing capabilities, Slurm has been the scheduling tool of choice for managing computing clusters.

ClearML Supports Seamless Orchestration and Infrastructure Management for Kubernetes, Slurm, PBS, and Bare Metal

Our early roadmap in 2024 has been largely focused on improving orchestration and compute infrastructure management capabilities. Last month we released a Resource Allocation Policy Management Control Center with a new, streamlined UI to help teams visualize their compute infrastructure and understand which users have access to what resources.

GenAI: Navigating the Risks That Come with Change

For enterprises, commercial use of AI is still in its early stages, and it’s a case of risk and reward, weighing up both and investigating the best way forward. Of course, there’s much to gain from the use of AI. Already, companies are providing better customer service, parsing complex information through natural language inputs, and generally making workflows faster.

Running Solid Queue in production Rails apps

Background jobs are essential to many Ruby on Rails apps. Since the introduction of ActiveJob, Rails developers have been able to manage their background jobs as natively as they do their database records. Still, ActiveJob requires you to select (and support) a backend adapter that will implement ActiveJob's backend. Many use Redis, a memory cache, to queue and process background jobs. Redis comes with incredible speed but is yet another dependency to maintain.

Accelerating Deployments of Streaming Pipelines - Announcing Data in Motion on Kubernetes

Organizations are challenged today to become both more data driven and more nimble to adapt quickly to changing conditions. These challenges are the driving forces behind much of their digital transformation or “modernization” efforts.