cs249r_book/mlsysim/docs/zoo/ops.qmd

---
title: "The Ops Zoo"
subtitle: "Operational Thresholds and Training-Run Profiles"
---

The Ops Zoo provides **operational anchors** for fleet operations — PSI drift thresholds,
KS-test coefficients, memory bit-error rates, and reusable training-run goodput-loss
profiles used in MLOps, robust-AI, and distributed-training chapters.

```{python}
#| echo: false
#| output: asis
import mlsysim

mon = mlsysim.Ops.Monitoring
rows = [
    ("PSI warn", mon.PsiWarnThreshold),
    ("PSI review", mon.PsiReviewThreshold),
    ("PSI critical", mon.PsiCriticalThreshold),
    ("KS coefficient", mon.KsTestCoefficient),
    ("Memory BER / bit", mon.MemoryBitErrorRatePerBit),
]
print("| Threshold | Value |")
print("|:---|:---:|")
for label, val in rows:
    print(f"| **{label}** | {val} |")

print()
print("## Training Run Overheads\n")
overheads = mlsysim.Ops.TrainingRunOverheads
print("| Overhead | Fraction | Description |")
print("|:---|:---:|:---|")
for item in overheads.list():
    name = getattr(item, "name", type(item).__name__)
    desc = getattr(item, "description", "") or "---"
    print(f"| **{name}** | {float(item):.2f} | {desc} |")
```

## Python Access

```python
import mlsysim

psi_warn = mlsysim.Ops.Monitoring.PsiWarnThreshold
psi_critical = mlsysim.Ops.Monitoring.PsiCriticalThreshold
checkpoint_overhead = mlsysim.Ops.TrainingRunOverheads.Checkpoint
```

These are assumption tables for teaching and appendix lineage — not live alerting defaults
for production systems.