Ml Project Management - Learning Module

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Launch Criteria

The Gates to Production

The most dangerous moment in an ML project is the transition from development to production. Models that performed excellently in offline evaluation have caused production outages, financial losses, and reputational damage when launched without proper safeguards.

Launch criteria are the systematic gates that a model must pass before serving real users. They protect against:

Performance failures — Models that underperform on production data
Reliability failures — Models that crash, timeout, or degrade infrastructure
Safety failures — Models that cause harm to users or the business
Compliance failures — Models that violate legal or regulatory requirements

This page provides a comprehensive launch readiness framework that enables confident, safe deployment.

What You Will Master

By completing this page, you will be able to: (1) Define comprehensive launch readiness criteria for ML models, (2) Design validation gates that catch issues before production impact, (3) Select appropriate deployment strategies for different risk profiles, (4) Implement monitoring that detects production issues early, and (5) Plan rollback procedures for failed launches.

Launch Readiness Assessment

A model is launch-ready when it has passed all required validation gates across five dimensions: Model Quality, Infrastructure Readiness, Safety & Fairness, Operational Readiness, and Stakeholder Alignment.

The Launch Readiness Checklist:

ML Model Launch Readiness Checklist
Dimension	Criteria	Evidence Required
Model Quality	Meets performance thresholds on holdout	Evaluation report with confidence intervals
Model Quality	Passes A/B test statistical significance	Experiment report with p-values
Infrastructure	Latency meets SLA requirements	Load test results
Infrastructure	Handles peak traffic without degradation	Stress test results
Safety	Fairness metrics within acceptable bounds	Fairness audit report
Safety	No harmful output patterns detected	Safety evaluation results
Operational	Monitoring dashboards operational	Dashboard screenshots
Operational	Runbooks documented for incidents	Runbook links
Stakeholder	Product owner sign-off obtained	Approval record
Stakeholder	Legal/compliance review complete	Review documentation

Quality Thresholds:

Explicit, quantitative thresholds must be defined for each quality dimension:

Metric	Minimum Threshold	Target	Blocking?
Primary model metric	Defined in scoping	Above baseline	Yes
Latency P50	< 50ms	< 20ms	Yes
Latency P99	< 200ms	< 100ms	Yes
Error rate	< 0.1%	< 0.01%	Yes
Fairness gap	< 10%	< 5%	Yes for sensitive applications

Blocking criteria must be met; non-blocking criteria are tracked but don't prevent launch.

No Exceptions Culture

Launch criteria lose their value if they're routinely waived. Every exception sets precedent. If criteria are legitimately too strict, update them formally—don't bypass them informally. Organizations with 'this one time' exception cultures experience more production incidents.

Validation Gates

A validation gate is a checkpoint that must be passed before proceeding to the next stage. Gates create hard stops that prevent premature launches.

The Gate Sequence:

Offline Evaluation Gate — Model meets quality thresholds on evaluation data
Infrastructure Gate — Model passes load and stress testing
Shadow Mode Gate — Model performs correctly on live traffic (without serving)
Canary Gate — Model performs correctly for small live traffic fraction
Ramping Gate — Model performs correctly at increasing traffic percentages
Full Launch Gate — Model ready for 100% traffic

launch_gates.py
Python
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from dataclasses import dataclass
from typing import Dict, List, Optional
from enum import Enum
 
class GateStatus(Enum):
    NOT_STARTED = "not_started"
    IN_PROGRESS = "in_progress"
    PASSED = "passed"
    FAILED = "failed"
    BLOCKED = "blocked"
 
@dataclass
class LaunchGate:
    name: str
    description: str
    criteria: Dict[str, float]  # metric: threshold
    evidence_required: List[str]
    blocking: bool = True
 
@dataclass
class LaunchReadinessReport:
    model_version: str
    gates: Dict[str, GateStatus]
    blocking_issues: List[str]
    warnings: List[str]
    ready_to_launch: bool
 
def evaluate_launch_readiness(
    model_version: str,
    evaluation_results: Dict[str, float],
    gate_definitions: List[LaunchGate]
) -> LaunchReadinessReport:
    """
    Evaluate model against all launch gates.
    """
    gate_statuses = {}
    blocking_issues = []
    warnings = []
    
    for gate in gate_definitions:
        passed = True
        for metric, threshold in gate.criteria.items():
            actual = evaluation_results.get(metric)
            if actual is None:
                passed = False
                if gate.blocking:
                    blocking_issues.append(
                        f"{gate.name}: Missing metric '{metric}'"
                    )
            elif actual < threshold:
                passed = False
                if gate.blocking:
                    blocking_issues.append(
                        f"{gate.name}: {metric}={actual:.3f} < {threshold}"
                    )
                else:
                    warnings.append(
                        f"{gate.name}: {metric}={actual:.3f} < {threshold}"
                    )
        
        gate_statuses[gate.name] = (
            GateStatus.PASSED if passed else GateStatus.FAILED
        )
    
    return LaunchReadinessReport(
        model_version=model_version,
        gates=gate_statuses,
        blocking_issues=blocking_issues,
        warnings=warnings,
        ready_to_launch=len(blocking_issues) == 0
    )
 
# Example gate definitions
STANDARD_GATES = [
    LaunchGate(
        name="Model Quality",
        description="Offline evaluation metrics",
        criteria={
            "auc_roc": 0.85,
            "precision_at_10pct": 0.50,
        },
        evidence_required=["evaluation_report.pdf"],
        blocking=True
    ),
    LaunchGate(
        name="Latency",
        description="Inference performance",
        criteria={
            "latency_p50_ms": 50,
            "latency_p99_ms": 200,
        },
        evidence_required=["load_test_results.json"],
        blocking=True
    ),
    LaunchGate(
        name="Fairness",
        description="Demographic parity",
        criteria={
            "max_group_gap": 0.10,
        },
        evidence_required=["fairness_audit.pdf"],
        blocking=True
    ),
]

Shadow Mode Is Essential

Shadow mode runs the new model on production traffic without serving results. It catches issues offline evaluation misses: feature computation bugs, data format mismatches, and unexpected edge cases. Run shadow mode for at least one business cycle (often one week) before any live traffic.

Deployment Strategies

The deployment strategy determines how traffic shifts from the old model (or no model) to the new model. Strategy selection depends on risk tolerance, rollback requirements, and infrastructure capabilities.

Strategy Comparison:

ML Deployment Strategy Comparison
Strategy	Risk Level	Rollback Speed	Best For
Big Bang	High	Slow	Low-stakes, simple models
Canary	Medium	Fast	Standard production models
Blue-Green	Medium	Instant	When instant rollback required
Rolling	Low	Gradual	Large-scale distributed systems
Feature Flag	Low	Instant	Fine-grained control needed

Canary Deployment Steps

•Deploy new model alongside existing
•Route 1-5% of traffic to new model
•Monitor key metrics for anomalies
•If healthy, increase to 10%, 25%, 50%
•At each stage, wait and validate
•Full rollout after all stages pass

Blue-Green Deployment Steps

•Maintain two identical environments
•Deploy new model to 'green' (inactive)
•Test green environment thoroughly
•Switch traffic from 'blue' to 'green'
•Monitor for issues post-switch
•Keep blue ready for instant rollback

A/B Testing as Deployment:

For models with measurable business impact, deployment can be structured as an A/B test:

Control group receives current model (or no model)
Treatment group receives new model
Statistical analysis determines winner
Winner promoted to 100% traffic

This approach provides causal evidence of model value but requires longer deployment timeline and sufficient traffic for statistical power.

Fast Rollback Is Non-Negotiable

Every deployment must have a tested rollback path. If the new model causes issues, you must be able to revert within minutes, not hours. Rollback procedures should be automated and tested before launch—never rely on untested manual procedures during an incident.

Production Monitoring Requirements

Launching is the beginning of monitoring, not the end of the journey. ML models degrade over time due to data drift, concept drift, and changing user behavior. Monitoring must detect issues before they cause significant harm.

The Monitoring Stack:

ML Production Monitoring Layers
Layer	What to Monitor	Alert Thresholds
Infrastructure	Latency, throughput, error rates, memory	P99 latency > 2× baseline
Data	Input distributions, missing values, schema	Distribution drift > threshold
Model	Prediction distributions, confidence scores	Unexpected prediction patterns
Business	Downstream metric impact	Significant metric degradation
Feedback	Ground truth when available	Accuracy drop below threshold

Key Monitoring Metrics

•Prediction distribution — Mean, variance, and percentiles of model outputs; sudden shifts indicate potential issues
•Feature distributions — Track drift in input features; significant changes may require model retraining
•Latency percentiles — P50, P95, P99 latency; degradation indicates infrastructure or model issues
•Error rates — Inference failures, timeouts, and exceptions; should remain near zero
•Confidence calibration — If model provides probabilities, verify they remain well-calibrated
•Business metrics — Downstream impact metrics; the ultimate measure of model value

Leading vs Lagging Indicators

Prediction distribution and feature drift are leading indicators—they signal potential problems before business impact. Business metrics are lagging indicators—by the time they move, damage is done. Monitor leading indicators for early warning; alert on lagging indicators for confirmed issues.

Incident Response and Rollback

Despite careful validation, production incidents happen. The quality of incident response determines whether a minor issue becomes a major outage.

The Incident Response Playbook:

Detection — Automated alerts or user reports trigger incident
Triage — Assess severity and impact scope
Containment — Stop the bleeding (often: rollback immediately)
Investigation — Identify root cause
Resolution — Fix the underlying issue
Post-mortem — Document learnings to prevent recurrence

Rollback Decision Matrix:

Rollback Decision Criteria
Severity	Symptoms	Action	Timeline
Critical	Complete model failure, revenue impact	Immediate rollback	< 5 minutes
High	Significant quality degradation	Rollback + investigate	< 15 minutes
Medium	Noticeable issues, limited impact	Investigate first, rollback if needed	< 1 hour
Low	Minor anomalies, no user impact	Monitor closely, no rollback	Monitor

Post-Deployment Checklist:

After every launch:

✓ Confirm monitoring dashboards show expected patterns ✓ Verify no anomalous alerts in first hour ✓ Check downstream systems for unexpected behavior ✓ Review sample predictions for sanity ✓ Confirm rollback mechanisms remain functional ✓ Document any observations or concerns

Post-Mortem Culture:

Every significant incident should produce a blameless post-mortem that documents:

What happened
Timeline of events
Root cause analysis
What worked well
What could be improved
Concrete action items with owners

When in Doubt, Roll Back

During incidents, err on the side of rolling back. The cost of unnecessary rollback (lost experiment time) is always less than the cost of extended production issues. You can always re-deploy after investigation; you can't undo user harm or revenue loss.

Summary and Key Takeaways

Key Takeaways

•Launch readiness spans five dimensions — Model quality, infrastructure, safety, operations, stakeholder alignment
•Validation gates prevent premature launches — Offline → Shadow → Canary → Ramping → Full
•Match deployment strategy to risk — Canary for standard models; blue-green for instant rollback needs
•Monitoring is post-launch essential — Track leading indicators (drift) and lagging indicators (business metrics)
•Rollback must be instant and tested — Never launch without a verified rollback path
•Incidents happen; response matters — Blameless post-mortems build organizational learning

Module Complete:

You have now completed the ML Project Management module. You understand problem scoping, data collection planning, experiment management, iteration strategies, and launch criteria—the comprehensive skill set that enables successful ML projects from inception to production.

These project management capabilities are as important as technical ML skills. Many models fail not due to algorithmic limitations but due to poor problem definition, inadequate data planning, chaotic experimentation, or premature launch. You now have the frameworks to avoid these failures.

Module Complete

Congratulations! You have mastered ML Project Management. From problem scoping to launch criteria, you now possess the systematic approach that distinguishes successful ML practitioners. Apply these frameworks to your projects, and continuously refine them based on your experience.