Hybrid MLOps¶

The “best of both worlds” approach—meaning twice as many things to go wrong.

What can possibly go wrong?¶

The groundhog day effect¶

Data scientists spend 80% of their time cleaning the same data, over and over, like Sisyphus with a Jupyter notebook.

• Models stagnate because no one has time to improve them.

• Talent attrition, as frustrated data scientists flee to “more innovative” companies.

• A LinkedIn thinkpiece asks: “Is MLOps Just Data Janitorial Work in Disguise?”

The monitoring mirage¶

The team builds a shiny dashboard to track model performance—then ignores it until a customer complains.

• Models decay silently, like fruit left in the back of the fridge.

• A post-mortem reveals the monitoring system was alerting the wrong Slack channel for months.

• Another AI system fails spectacularly, prompting calls for “better oversight” (which will also be ignored).

The collaboration conundrum¶

Data scientists, engineers, and DevOps all agree on best practices—in separate meetings, with contradictory outcomes.

• Inconsistent deployments, where “tested” models behave differently in production.

• Endless debates about whether Kubernetes is “overkill” (it is) or “necessary” (it might be).

• A tech conference panel debates “Why Can’t Data People Just Get Along?”

Hallmarks of hybrid ops¶

• The team has all the right roles—but they operate like rival factions in a particularly petty medieval court.

• Collaboration tools are plentiful (Slack, Jira, Notion), yet communication still happens via cryptic commit messages.

• Big data is involved, but only because no one could agree on what “small data” means anymore.

• Open-source tools are both loved and loathed—used in production, but only after a nervous 2 a.m. Stack Overflow deep dive.