Aleksandra.Kulinska

Unlocking Cloud Economics: Mastering FinOps for Smarter Cost Optimization The FinOps Framework: A Strategic Blueprint for Cloud Economics The FinOps framework provides a structured, iterative approach to managing cloud financial operations, transforming cost from a static accounting function into a dynamic engineering variable. It is built on three continuous, interconnected phases: Inform, Optimize, and Operate. […]

Unlocking Cloud Economics: Mastering FinOps for Smarter Cost Optimization The FinOps Framework: A Strategic Blueprint for Cloud Economics At its core, the FinOps framework is a cultural practice and operational model that brings financial accountability to the variable spend model of the cloud. It’s a strategic blueprint where engineering, finance, and business teams collaborate to

Data Engineering with Great Expectations: Building Trustworthy Data Pipelines What is Great Expectations and Why It’s Essential for data engineering Great Expectations is an open-source Python library designed to validate, document, and profile your data. It acts as a data testing framework, allowing engineers to define „expectations”—assertions about data quality—such as ensuring a column contains

Data Engineering with DuckDB: The In-Process OLAP Engine Revolution What is DuckDB and Why It’s a Game-Changer for data engineering DuckDB is an in-process analytical database (OLAP) embedded directly into applications, eliminating the need for separate database servers. It reads and writes Parquet, CSV, and JSON files directly, functioning as a powerful SQL engine over

Unlocking Cloud Economics: Mastering FinOps for Smarter Cloud Cost Optimization What is FinOps? The Financial Operating Model for the Cloud FinOps, short for Financial Operations, is a strategic operating model and cultural practice that unites finance, technology, and business teams to collaboratively manage and optimize cloud costs. The goal is not merely cost reduction but

Beyond the Hype: Building Sustainable MLOps for Long-Term AI Success The mlops Imperative: From Experiment to Enterprise Asset Transitioning a machine learning model from a research notebook to a reliable, scalable enterprise asset is the core challenge MLOps addresses. Without a systematic approach, models decay, deployments become fragile, and business value evaporates. This process requires

Demystifying Data Science: A Beginner’s Roadmap to Actionable Insights What is data science? The Engine of Modern Insight Data science is the interdisciplinary field dedicated to extracting knowledge and actionable insights from both structured and unstructured data. It synthesizes statistics, computer science, and domain expertise to solve complex, real-world problems. For IT and data engineering

Beyond Automation: The Human Element in MLOps Collaboration and Culture The mlops Imperative: Why People Are the Ultimate Orchestrators While automation tools are essential for scaling machine learning, the true orchestration of a successful ML system relies on human expertise. This is where the strategic guidance of consultant machine learning professionals becomes invaluable. They architect

MLOps on a Budget: Building Cost-Effective AI Pipelines for Production The Core Principles of Budget-Conscious mlops The foundation of cost-effective AI lies in automation and standardization. Automating repetitive tasks—such as data validation, model training, and deployment—eliminates manual toil and reduces errors that lead to rework. Standardizing project structure, experiment tracking, and model packaging ensures reproducibility

Beyond the Pipeline: MLOps for Model Governance and Ethical AI Why Model Governance is the Critical Next Phase for mlops While robust MLOps pipelines automate training and deployment, they often lack the controls to ensure models remain fair, compliant, and reliable in production. This gap is where model governance becomes the indispensable next phase. It’s