Dagster for AI & ML Pipelines: What Works, What Breaks, and Why We Chose It
Автор: deepsense
Загружено: 2026-01-22
Просмотров: 27
In this AI Tech Experts Webinar, Nikodem Tadrowski, ML Engineer, shares practical lessons from adopting Dagster as a framework for production-grade data preparation pipelines.
The talk walks through real challenges faced by the team when creating a single source of truth for tabular data and feature logic, and explains why Dagster was chosen over tools like Airflow, Prefect or Luigi.
🔹 why repeated preprocessing breaks ML reliability
🔹 Dagster’s asset-centric model and data lineage tracking
🔹 core concepts: assets, ops, graphs and components
🔹 how metadata, typing and observability help catch issues early
🔹 common pitfalls when abstracting pipelines too early
🔹 when Dagster works well — and when it doesn’t
If you have questions for Nikodem, feel free to ask them in the comments and continue the discussion there!
01:12 Why This Topic?
02:50 What are the available options for Python?
05:33 What does Dagster solve out of the box?
07:30 Core Dagster concepts: assets, ops, graphs
18:49 Lessons learned and final conclusions
Check our website: https://deepsense.ai/
Linkedin: / applied-ai-insider
#Dagster #DataPipelines #MLPipelines #AIEngineering #DataLineage #FeatureEngineering #Python #MachineLearning #MLOps #DataWorkflows
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