Latest blog posts by Paweł Szczepanik

Models that never leave the notebook are a cost, not an experiment. A buyer's guide to MLOps consulting services: what they cover, when you need them, and how to choose a partner that gets models into production.

Databricks vs Snowflake in 2026: architecture, pricing, AI workloads, and lock-in compared, with a simple rule for choosing one or running both.

Most AI projects fail on data, not models. A practical executive guide to building AI-ready data infrastructure in 2026 — layers, pitfalls, and a 90-day path.

ETL process optimization cuts cost, speeds delivery, and lifts data quality. Core techniques, tooling, a 30-60-90 plan, and how to measure the payoff.

Data science analytics services turn enterprise data into decisions that move revenue, cost, and risk. See what they solve and how to measure the value.

Data science and machine learning solve different problems. This guide shows where each one creates business value and when you actually need a model.

A practical, engineering-first guide to building a data science pipeline that holds up in production, from ingestion to monitoring.

A practical look at what a data science consultancy delivers, how engagements run, and how to pick a partner that ships measurable business results.

What data science consulting services deliver for enterprises: pipelines, modeling, and MLOps that turn data into decisions you can defend and maintain.