Hamza Tahir - Why ML in production is STILL broken?
Speaker Bio -
Hamza Tahir is the CTO at maiot GmbH.
Talk Abstract -
Around 87% of machine learning projects do not survive to make it to production. There is a disconnect between machine learning being done in Jupyter notebooks on local machines and actually being served to end-users to provide some actual value.
The oft-quoted Hidden Technical Debt paper, by Scully et. al., has been in circulation since 2017, yet still, ML in production has ways to go to catch up to the quality standards attained by more conventional software development.
This talk will aim to break down the key aspects of what sets machine learning apart from traditional software engineering, and how treating data as a first-class citizen is a fundamental shift in our understanding of complex production ML systems.
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