AI on ThecoreGrid focuses on production-grade engineering for machine learning and LLM systems in highload environments.
We cover how to design scalable AI architectures, build reliable data and feature pipelines, and choose infrastructure for training and inference with predictable latency, cost, and resilience. The content is curated from real BigTech practices: incident post-mortems, MLOps and DevOps patterns, observability, security, and governance for AI-powered products. Instead of hype or beginner tutorials, you get deep technical analysis of real-world implementation: LLM integration into existing services, RAG architecture decisions, orchestration strategies, vector databases, caching, CI/CD for ML, and model quality control in production. The AI tag is built for architects, ML engineers, backend/platform teams, and SREs who deploy AI in critical systems and need robust, maintainable, and scalable solutions.
Accurate data provenance at the record and token level bridges the gap in AI unlearning: how to find the forget set without mass data deletion. The problem arises at the moment of consent withdrawal by the author. Unlearning algorithms (e.g., NPO or RMU) expect a ready forget set, but in real pipelines, it does not … Read more
GKE Agent Sandbox and hypercluster: how Kubernetes becomes a runtime for AI agents and addresses isolation, scale, and latency.
Multitenant GPU isolation in AI infrastructure: how to balance performance, security, and utilization across hardware, fabric, and orchestration layers.
AI compute infrastructure as the foundation for scaling models. An analysis of Stargate, architecture, partnerships, and growth constraints.
KV cache restoration in LLM serving: how 3D parallelism reduces TTFT and eliminates bottlenecks in compute and I/O. –>
How optimizing split learning through SFC reduces latency in distributed AI by jointly managing placement and routing
A selection of architectural insights and releases we read this week Infrastructure 🔹 DataCenterGym: A physics-informed simulator for multi-objective data center scheduling. The tool allows modeling and optimizing resource allocation in data centers, taking into account physical constraints and multiple objectives, significantly improving management efficiency. Read the release 🔹 Spot-and-Scoot: Investigating spot instance availability. A methodology … Read more
6-12 month IT trend analysis: why AI is becoming a runtime platform, security is shifting to Identity-First, and the industry is choosing efficiency
AI agent memory as an architectural layer. How persistent memory eliminates stateless limitations and impacts system scalability
How AI code review in CI/CD reduces latency and noise through the orchestration of LLM agents and strict filtering of results
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