Event

FREE data + AI knowledge sessions to help each others and learn from each other, share job opportunity

Session 3: Data Pipelines & Quality — AI-Powered Validation and Anomaly Detection
Pipelines, ingestion, and the step everyone skips: validation. Plus how LLMs can now detect data anomalies that rule-based checks miss.
Demo (~15 min): Airflow DAG chain. Run a validation script comparing computed metrics against a reference source — all matching at 0.0 difference. Show the 40-point bug story and the fix.
AI Hands-on (~15 min): Feed a messy sample dataset to an LLM: "Here are 50 rows of data. Find anomalies, missing values, and anything suspicious." Compare what Gemini vs Claude catches. Discuss: would you trust AI for data quality in your pipeline?
Open Floor (~20 min): Worst data quality horror story? What validation patterns work at scale? Anyone using AI for data profiling or documentation? What do you wish upstream teams did differently? Any job opportunny to share

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| Session | Special Topic | AI Component |
| ------- | ------------- | ------------ |
| 1 | Data & AI Ecosystem overview | First prompt challenge — everyone builds something |
| 2 | Cloud Infrastructure (AWS/GCP/Snowflake) | Vibe-code a Dockerfile + docker-compose with AI |
| 3 | Data Pipelines & Quality | AI anomaly detection on messy data |
| 4 | Analytics & BI | Text-to-SQL — ask your database in English |
| 5 | ML Evaluation & Backtesting | AI as model critic — "what's wrong with my eval?" |
| 6 | LLMs Deep Dive | Live model shootout — 4 models, same prompt, group scores |
| 7 | AI Agents — Tools, RAG, Memory | Vibe-code a working agent together |
| 8 | Multi-Agent Systems | Group design exercise — sketch a multi-agent for your problem |
| 9 | Vibe Coding Deep Dive | Build a complete feature live with AI |
| 10 | Show-and-Tell & Opportunities | Community demos + jobs + what's next |