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The Persistent Peril of Machine Learning

In our ML & Data trends post from February we discussed whether one believes MLOps has crossed the chasm or not, the rise of MLOps (i.e. DevOps for ML) signals an industry shift from PoC’s (how to build models) to operations (how to run models). Even though this shift is something that we’re extremely excited about, there’s a recurrent bottleneck that keeps haunting us year after year: data quality.

Adapted from O’Reilly (2019)
Image courtesy of Rackspace Technologies (2021)

We need data engineers

Let’s show data engineers some love

The pandemic highlighted our ML vulnerabilities

Example of a non-critical failure still resulting in a financial loss

Machine Learning vs Software Engineering

Image courtesy of Matei Zaharia/Databricks
Image courtesy of Hawaiian News

Your model was never your IP, it’s your data

Image courtesy of Stanford University/Chip Huyen



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Oliver Molander

Co-founder at Validio and early-stage tech investor at J12 Ventures. Preaching about the realities & possibilities of Data & ML.