Module 4 Assignment: Distributed processing and scale

Scenario

You are advising a platform team designing data infrastructure for repeatable model training and monitoring. The stakeholders are: data engineer, ML engineer, security architect, data steward, and platform owner.

Task

Answer the module question: When does scale require distributed computation?

Use the module lab and course readings to produce: AI data platform design review with lineage, quality checks, cost controls, and access model focused on distributed processing and scale: Profile batch processing and identify bottlenecks..

Required Evidence

• Define the decision or system boundary in one paragraph.

• Identify the dataset, proxy data, or evidence source you used: synthetic pipeline events with freshness, schema drift, lineage completeness, volume, and access-risk indicators.

• Compare at least two alternatives, baselines, policies, or designs.

• Report one quantitative result or structured scoring table.

• Explain two failure modes and one mitigation for each.

• State what additional evidence would be required before real deployment.

Submission

Submit the completed notebook plus a 900-1200 word memo. The memo must include clear headings for context, method, evidence, risks, recommendation, and open questions.

# Assignment workspace for Module 4: Distributed processing and scale
module = 4
decision = "When does scale require distributed computation?"
artifact = "AI data platform design review with lineage, quality checks, cost controls, and access model focused on distributed processing and scale: Profile batch processing and identify bottlenecks."

alternatives = [
    {"option": "baseline_or_manual_process", "strength": "", "risk": "", "evidence": ""},
    {"option": "ai_assisted_or_advanced_option", "strength": "", "risk": "", "evidence": ""},
]

recommendation = {
    "decision": decision,
    "recommended_option": "",
    "minimum_evidence_before_pilot": [],
    "monitoring_metric": "",
    "rollback_trigger": "",
}

{"module": module, "artifact": artifact, "alternatives": alternatives, "recommendation": recommendation}

{'module': 4,
 'artifact': 'AI data platform design review with lineage, quality checks, cost controls, and access model focused on distributed processing and scale: Profile batch processing and identify bottlenecks.',
 'alternatives': [{'option': 'baseline_or_manual_process',
   'strength': '',
   'risk': '',
   'evidence': ''},
  {'option': 'ai_assisted_or_advanced_option',
   'strength': '',
   'risk': '',
   'evidence': ''}],
 'recommendation': {'decision': 'When does scale require distributed computation?',
  'recommended_option': '',
  'minimum_evidence_before_pilot': [],
  'monitoring_metric': '',
  'rollback_trigger': ''}}

Acceptance Criteria

Your submission is complete only if another reviewer can reproduce your reasoning from the evidence you provide. You do not need production-grade data, but you must be explicit about proxy-data limits and what would change with real institutional data.