Sep 17, 2026 · 5 min · 8 segments
Most ML projects die somewhere between a promising notebook and a live system. This field guide identifies the people, projects, and organisations whose work reveals exactly where the gaps are and…
Because a notebook is a sandbox and production is a war zone.
In the notebook, that's the interactive coding environment where data scientists prototype.
Everything is clean, static, and forgiving.
In production, the data is late.
The users are weird.
And nobody warned you the upstream system changed a column name at 2 a.m.
So it's not that the models are bad.
It's everything around them.
The model is maybe 10% of the work.
The other 90 is plumbing, pipelines that move data, monitoring that tells you when it rots, and the boring governance of who owns what.
O'Reilly's survey work has been saying this for years.
The top barriers to ML adoption aren't algorithms.
They're skills gaps and lack of data infrastructure.
Because a notebook is a sandbox and production is a war zone.
In the notebook, that's the interactive coding environment where data scientists prototype.
Everything is clean, static, and forgiving.
In production, the data is late.
The users are weird.
And nobody warned you the upstream system changed a column name at 2 a.m.
So it's not that the models are bad.
It's everything around them.
The model is maybe 10% of the work.
The other 90 is plumbing, pipelines that move data, monitoring that tells you when it rots, and the boring governance of who owns what.
O'Reilly's survey work has been saying this for years.
The top barriers to ML adoption aren't algorithms.
They're skills gaps and lack of data infrastructure.
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