Data Analysis AI Systems DevOps Foundations
Barbara Górska Sennerstam
Focused on building end-to-end projects involving Data Processing, Analytics, ML Workflows, and system-level thinking — turning raw data into reliable applications and infrastructure.
Currently expanding into DevOps / MLOps to better understand Deployment & Infrastructure.
Data analysis → AI/ML workflows → deployment fundamentals
Work
A focused set of projects spanning process automation, BI infrastructure, privacy-aware data tooling, AI-assisted analytics, dashboard work, and deployment fundamentals.
Automation + BI Infrastructure
A local Docker Compose analytics and automation platform combining PostgreSQL, n8n, and Metabase to ingest business data, validate webhook payloads, log workflow executions, and monitor operations through dashboards.
Local system flow
Privacy-aware data application
A local-first anonymization app designed to reduce disclosure risk while preserving analytical usefulness.
AI-powered analytics & forecasting app
A business analytics application combining data processing, ML forecasting, cloud storage, and an AI assistant for KPI questions.
Dashboard and reporting project
Business-oriented dashboards focused on clear reporting, structured visuals, and decision-support insights.
Current development focus
Hands-on learning around Linux, SSH, Docker, deployment workflows, CI/CD basics, and infrastructure fundamentals.
Selected case studies
System-focused project deep-dives showing how data moves from raw inputs into usable applications, automation workflows, dashboards, and operational feedback loops.
A local analytics and automation stack for business data ingestion, workflow logging, and operational dashboards.
A reproducible Docker Compose portfolio deployment showing how PostgreSQL, n8n, and Metabase can work together to support business analytics, webhook automation, workflow execution logging, and workflow observability.
System thinking
n8n receives webhook payloads, checks a shared secret header, validates incoming JSON, and returns structured HTTP responses.
PostgreSQL stores business records and workflow execution logs, including success, unauthorized, and validation-error outcomes.
SQL views prepare the data for Metabase dashboards that show business KPIs and workflow health.
Architecture
Valid payload is written to PostgreSQL and logged as a successful workflow execution.
Missing or invalid webhook secret is rejected and stored as an unauthorized execution.
Authorized but invalid payload is rejected, logged, and returned with a structured error response.
System signal
System signal
System map
Privacy-aware anonymization workflow
A local-first Streamlit app for privacy-preserving dataset anonymization, risk review, and export reporting.
System thinking
Detects PII signals, classifies columns, and identifies direct, indirect, and sensitive attributes.
Plans anonymization actions using policy context and lets the user review protection choices.
Applies transformations, checks residual risk, and generates reproducible export reports.
System map
Analytics, forecasting & AI assistant
A retail analytics and forecasting app combining validation pipelines, cloud model artifacts, LLM-assisted queries, and observability.
System thinking
Loads raw data from cloud storage, preprocesses transactions, and validates schema consistency.
Builds weekly features, trains bundled KPI forecasting models, and stores model artifacts.
Streamlit app loads artifacts, serves forecasts, and routes natural-language analytics questions.
Foundation
Before moving deeper into ML, AI, and deployment workflows, I completed a Power BI reporting project through practical data analysis training, covering SQL, dashboard design, KPI reporting, and business-focused visual communication.
Current focus
I am currently aiming to build practical DevOps and MLOps foundations. The goal is to strengthen the infrastructure side of my AI/ML work through Linux, WSL, SSH, Docker, Git workflows, CI/CD basics, Kubernetes fundamentals, and deployment-oriented project practice.
This connects my data and AI projects with the operational skills needed to make applications reproducible, maintainable, and production-aware.
Hands-on foundations
Applied through projects
Deployment-aware AI/ML
Infrastructure literacy
I have already applied parts of this skill set in my data science and AI projects through WSL, terminal workflows, Git, VS Code Remote, environment files, cloud-hosted assets, and deployment practice. Next, I am expanding into containers, Kubernetes fundamentals, networking, and a hands-on homelab environment.This path supports my goal of bridging data analysis, AI/ML projects, and deployment workflows into reproducible, deployable, and maintainable systems.
ABOUT ME
I am developing a practical profile around data, AI applications, and the delivery workflows needed to make them reproducible, usable, and maintainable.
Current direction
Data + AI systems delivery
Building projects that connect data analysis, AI/ML workflows, and deployment foundations into usable, maintainable systems.
Business foundation
My background is in economics and business-oriented problem solving, which shaped my progression toward data analysis, including SQL, Power BI, KPIs, dashboards, and reporting. This foundation helped me understand how business questions become data workflows and how insights need to be communicated clearly.
AI / ML applications
From there, I moved into Python, data science, machine learning workflows, and AI-assisted applications. At that stage, I learned how data preparation, models, and prediction workflows are built and how to package them into useful interfaces and assistants.
System understanding
However, my focus is not only on building models or interfaces, but on understanding how data moves through a system — from raw input and validation to model logic, application behavior, and user-facing output.
Delivery direction
Currently, I am building practical DevOps and MLOps foundations to strengthen the infrastructure side of my data and AI/ML projects. I am focusing on learning Linux, Git workflows, Docker, CI/CD basics, Kubernetes fundamentals, environment management, and deployment-oriented project structure.
Goal
My goal is to bridge data analysis, AI/ML projects, and deployment workflows into practical systems that are clear, reliable, and easier to maintain.
Learning path
Working approach
I use AI tools as a learning and development accelerator, while focusing on understanding architecture, code structure, debugging, deployment constraints, and system behavior.
