This article walks through building a modern Databricks pipeline using the Medallion Architecture, explains Delta Lake's transaction log and ACID guarantees.
R&D succeeds when teams build just enough infrastructure to validate the highest-risk technical assumptions without over-engineering or over-researching the problem.
RAG prototypes are easy. Production RAG is not. This covers vector DB tradeoffs, chunking patterns, re-ranking, and evaluation setups that hold up at scale.
A five-minute walkthrough for adding logs, traces, and error monitoring to a React Native iOS app using LaunchDarkly's Observability SDK, shown on a simple counter app.
A practical guide to feature engineering at scale with Azure Databricks, covering distributed data processing with Spark and reliable storage with Delta Lake.
Constraint-based background work, foreground services, push topic subscriptions, shared-content handling, and enhanced local notifications with full simulator support.
AI-generated code introduces integration failures that spec-based tests cannot catch. Regression testing grounded in real production behavior is the fix.
Static thresholds fail in complex distributed systems. This article introduces a context-aware control loop architecture to isolate failures and automate recovery.
AI infrastructure isn’t about GPUs. Most issues come from storage, networking, data pipelines. If GPU utilization is low, check the infrastructure first, not the model.