Episode Details

Why do most machine learning projects never leave the lab? The position here is blunt: the model is about ten percent of the work and the other ninety percent is pipelines, monitoring and ownership. The episode walks through the three failure points at the notebook to production handoff, data drift, unowned pipelines after launch, and systems never designed for retraining, and why a drifting model costs money quietly instead of paging you at midnight.

You come away able to name the roles that fix this, the ML engineer and the data engineer, read vendor claims from DBT Labs against O'Reilly Radar and MIT Sloan Management Review, apply data as a product, and ask the accountability question before funding an AI project.

Key takeaways

• Before approving any model, ask who by name is on the hook to keep it running six months after launch.
• Budget for an ML engineer who sits between the data scientist and the software team, not just modellers.
• Treat each dataset as a product with an owner, a quality standard, documentation and a promise it will not silently change.
• Monitor for data drift, because a degrading model keeps returning confident answers instead of crashing.
• Discount vendor productivity claims such as DBT Labs surveys and cross check them against O'Reilly Radar or MIT Sloan research.

Chapters
0:00 Why ML projects die after the notebook
1:10 Three places the handoff snaps
2:02 The ML engineer nobody budgets for
2:25 Data as a product, not byproduct
3:25 Why buying a platform does not help
4:07 The one question to ask before greenlighting

Go deeper, free lessons

• Closing the POC-to-production gap
• Models in production: drift, monitoring & MLOps
• MLOps: monitoring, retraining & drift
• The lakehouse: unifying analytics & ML
• Data observability: detect problems before your users

Sources

• Building a Data Lakehouse with DuckDB and DuckLake
• What’s So Good About ChatGPT Work? Here’s What I Found
• 5 Free Zoomcamps From Data Pipelines to AI Agents
• “Everyone’s in a race to replace GitHub”: Zed launches Delta because agents made pull requests obsolete
• Fivetran + dbt Labs Announces New Capabilities to Make Enterprise Data Agent-Ready at dbt Summit 2026
• Everything we announced at dbt Summit and why it matters
• We built dbt State to stop rebuilding what hadn't changed
• Celebrating the 2026 dbt partner of the year winners

The article: https://www.mba-training.com/en/blog/ml-pilots-production-field-guide

MBA Training, mba-training.com