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Why ML in Production is Still Broken

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

Tags

Machine Learning Courses Data Science Courses MLOps Courses Jupyter Notebooks Courses Software Engineering Courses Data Management Courses Data Engineering Courses Model Deployment Courses Technical Debt Courses

Course Description

Overview

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Explore the challenges of implementing machine learning in production environments in this 44-minute conference talk by Hamza Tahir, CTO at maiot GmbH, presented at the Toronto Machine Learning Series (TMLS). Discover why 87% of machine learning projects fail to reach production and the disconnect between development in Jupyter notebooks and real-world application. Examine the key differences between machine learning and traditional software engineering, and learn why treating data as a first-class citizen is crucial for successful production ML systems. Gain insights into the ongoing struggle to meet quality standards in ML production, despite the circulation of the influential Hidden Technical Debt paper since 2017.

Syllabus

Hamza Tahir - Why ML in production is STILL broken?


Taught by

Toronto Machine Learning Series (TMLS)

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