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  1. Asked: January 1, 2026In:

    How do I manage multiple models for the same prediction task?

    Dutch
    Dutch Begginer

    This is a governance and orchestration problem. Use clear evaluation criteria aligned with business goals. In some cases, ensemble or routing strategies perform better than a single model. Centralize deployment ownership and define decision rules for model selection. Avoid letting models compete silRead more

    This is a governance and orchestration problem.

    Use clear evaluation criteria aligned with business goals. In some cases, ensemble or routing strategies perform better than a single model.

    Centralize deployment ownership and define decision rules for model selection.

    Avoid letting models compete silently in production.

    Common mistakes include:Deploying models without ownership, Lacking comparison benchmarks andAllowing configuration sprawl

    The takeaway is that model choice should be intentional, not political.

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  2. Asked: December 26, 2025In:

    How do I design ML pipelines that are easy to debug?

    Dutch
    Dutch Begginer

    Debuggable pipelines favor transparency over cleverness. Break pipelines into clear, observable steps with explicit inputs and outputs. Log metadata at each stage and persist intermediate artifacts where feasible. Avoid monolithic jobs that hide failure points. Common mistakes include: Over-optimiziRead more

    Debuggable pipelines favor transparency over cleverness.

    Break pipelines into clear, observable steps with explicit inputs and outputs. Log metadata at each stage and persist intermediate artifacts where feasible.

    Avoid monolithic jobs that hide failure points.

    Common mistakes include:

    • Over-optimizing pipelines too early

    • Skipping intermediate outputs

    • Logging only errors

    The takeaway is that debuggability is a design choice, not an afterthought.

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  3. Asked: January 1, 2026In:

    What’s the biggest mistake teams make when moving ML to production?

    Dutch
    Dutch Begginer

    The takeaway is that production ML is a systems discipline, not just an algorithmic one. The biggest mistake is treating production ML as a modeling problem only. Production success depends on data quality, monitoring, deployment discipline, and ownership. Ignoring these leads to fragile systems. StRead more

    The takeaway is that production ML is a systems discipline, not just an algorithmic one. The biggest mistake is treating production ML as a modeling problem only.

    Production success depends on data quality, monitoring, deployment discipline, and ownership. Ignoring these leads to fragile systems.

    Start designing for production from day one, even during experimentation.

    Common mistakes include: Prioritizing accuracy over reliability, Ignoring monitoring, Lacking clear ownership

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