Latest blog posts by Paweł Szczepanik

DS Stream joins NASSCOM Agentic AI Confluence 2026 in Bengaluru on September 24. Meet us to talk decision intelligence for retail and CPG.

An AI governance framework that runs inside the delivery pipeline, with blocking, advisory and human gates, guardrails and evidence from every build.

An AI factory reference architecture built from logical layers, not GPU racks: five layers, two planes, the contract each exposes, mapped to any cloud.

Model monitoring in production has to catch decay, not just drift. Four signals, the label latency problem, thresholds that hold, and alerts people act on.

AI factory cost depends on integration, data work, governance and run rate more than GPUs. Seven budget categories, build vs run rate, cost per use case.

An AI center of excellence sets standards; an AI factory delivers them. Here is where each model breaks and what has to exist before the CoE can step back.

An AI data platform is judged by the workloads it runs: training, inference, vector retrieval, feature serving and model governance. Here is how to choose.

An AI use case pipeline turns scattered ideas into a funded portfolio: structured intake, scoring, stage gates and ROI tracked from baseline to production.

An AI maturity assessment scores capability across seven dimensions, not one score. See what each level looks like in delivery terms and what to fix first.

AI-ready data foundations decide which AI projects ship: four readiness dimensions, a use-case-scoped audit and how to price the gaps.