Latest blog posts by Paweł Szczepanik

Your MLOps stack ships classical models fine and still cannot tell you whether an LLM feature is improving. What LLMOps consulting covers, six signals you need it, and how to pick a partner.

What you actually buy in an enterprise RAG project: engagement phases, why demos stall before production, custom vs RAG-as-a-Service, retrieval quality and cost drivers.

Agent projects usually stall during scoping. What AI agent development services include, how autonomy drives cost, typical market price ranges, delivery timelines and the ROI metrics that matter.

A delivery-team view of GenAI proof of concept development: production-ready success criteria, six failure points between pilot and launch, and a 90-day roadmap with gates.

How data governance for AI extends classic governance into training and RAG, what the EU AI Act requires in practice, and how to operationalize catalog, policy, and audit.

How to build and operate a production real-time data pipeline: core components, correctness patterns such as exactly-once and backpressure, latency SLOs, monitoring, and operational cost.

A decision framework for choosing between batch and streaming, scored on data freshness, volume, consumer expectations, and operational cost, with a look at where micro-batch, Kappa, and hybrid designs fit.

A practical guide to data quality for machine learning: the dimensions that matter, pipeline testing with contracts and dbt, drift, and how to operationalize it.

What generative AI consulting services cover, from use-case strategy and RAG to governance and LLMOps, and why they move pilots into production.

A decision framework for in-house vs outsourced data engineering, covering total cost, knowledge risk, scalability, and the hybrid model.