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Building Machine Learning Pipelines in Airflow with Jupyter Notebooks

Offered By: Toronto Machine Learning Series (TMLS) via YouTube

Tags

Data Science Courses Machine Learning Courses Jupyter Notebooks Courses Scalability Courses Data Pipelines Courses Parameterization Courses

Course Description

Overview

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Learn how to develop end-to-end machine learning pipelines using Jupyter Notebooks and Airflow in this 38-minute conference talk from the Toronto Machine Learning Series. Discover a pattern for implementing each component of the pipeline in a notebook, parameterizing it with Papermill, and then scaling and scheduling the entire process using Airflow. Gain insights from experienced data scientists Palermo Penano and Kenneth Lau as they share their expertise in building maintainable and scalable machine learning solutions for financial services applications.

Syllabus

Building Machine Learning Pipelines in Airflow with Jupyter Notebooks


Taught by

Toronto Machine Learning Series (TMLS)

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