Chapter 4: ML Workflow & First Models
ML Workflow & First Models in ML Software Engineering: Interview Concept Review.
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Learning Objectives
By the end of this chapter, you will be able to:
- Relate ML Workflow & First Models to common ML software engineering interview questions and trade-offs.
- Explain when this topic deserves a deeper pass through another tutorial on this site versus staying at recap depth.
- Surface assumptions, pitfalls, and follow-up probes an interviewer is likely to use.
Problem framing aloud
List goal metric (business-aligned), fairness constraints, acceptable latency, freshness, labeling budget.
Baselines? Start with logistic regression / shallow tree depending on modality; articulate why—they calibrate intuition before heavyweight stacks.
Iterate like an engineer
Change one axis per experiment—data augmentation vs architecture vs LR—document hypothesis + observed delta + decision.
Articulate rollback plan if production metric dips (shadow deployments later chapter).
Go deeper on this site
Machine Learning Fundamentals chapters cover regression/classification scaffolding.
1. Strongest justification for logistic regression baseline before boosted trees?