Description
This talk will provide a practical deep dive on how to build industry-ready machine learning and data pipelines in Python. I will cover practical presentation 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.