Dagster for AI & ML Pipelines: What Works, What Breaks, and Why We Chose It скачать в хорошем качестве

Dagster for AI & ML Pipelines: What Works, What Breaks, and Why We Chose It

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