Posts and interview questions tagged Kubernetes.
The MLOps playbook: deploying models (REST/batch/streaming), versioning + registry + CI/CD, drift monitoring, cloud strategies, and a full end-to-end app.
How Mattrx swapped its Python ML service for ML.NET — regression, classification, and clustering trained in C#, in-process, no PhD required.
Full C# RAG chatbot: Semantic Kernel + Azure AI Search hybrid + reranker + streaming + tenant security. Mattrx Help: 4% hallucination, 91% recall.
Agentic vs Traditional RAG in .NET — when each wins, full Semantic Kernel code, the router that saved Mattrx $10,900/month on AI bills.
A system design walkthrough of YouTube: resumable upload, the transcoding pipeline, blob storage, CDN delivery, and adaptive bitrate streaming at scale.
A system design walkthrough of Uber: geospatial indexing (geohash/quadtree/H3), finding nearby drivers, rider matching, live location, and surge pricing.
A system design walkthrough of a payment system: idempotency, the double-entry ledger, exactly-once processing, and reconciliation with the provider.
A system design walkthrough of WhatsApp: WebSocket delivery, message ordering, delivery and read receipts, online presence, and group fan-out at scale.
A system design walkthrough of a news feed like Twitter or Facebook: fan-out on write vs read, the celebrity problem, ranking, and caching at scale.
A system design walkthrough of a notification system: multi-channel fan-out (push, SMS, email), queues, retries, dedup, and delivery at scale.
A system design walkthrough of a distributed cache like Redis/Memcached: consistent hashing, eviction policies, replication, and write strategies.
A system design walkthrough of a rate limiter: token bucket vs sliding window, where it lives, and making limits work across a distributed fleet.