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    Day 1


This talk will provide a practical deep dive on how to build industry-ready machine learning and data pipelines in Python. I will cover a hands-on case study that will build from the basics of Airflow, and show how it is possible to build scalable and distributed machine learning data pipelines using a distributed architecture with a producer- consumer backend using Celery. I will provide insights on some of the key learnings I have obtained throughout my career building machine learning systems, as well as caveats and best practices deploying scalable data pipelines systems in production environments.

Associated Speakers:

Alejandro Saucedo

Chief Scientist

Institute for Ethical AI & Machine Learning

Associated Talks:

04:00PM - Day 1

View Industry-ready data & machine learning pipelines

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