from google.cloud import aiplatform

# Define features ONCE

feature_store = aiplatform.Featurestore.create("wxcc-features")

entity_type = feature_store.create_entity_type(
    entity_type_id="customer",
    description="Customer-level features"
)

## Create features (computed once, reused by all models)

entity_type.create_feature(
    feature_id="avg_call_duration_30d",
    value_type="DOUBLE",
    description="Average call duration in last 30 days (seconds)"
)

entity_type.create_feature(
    feature_id="total_calls_90d",
    value_type="INT64",
    description="Total calls in last 90 days"
)

## Models fetch features from Feature Store (consistent, cached)

features = entity_type.read(entity_ids=["customer_12345"])

Benefits: - Consistency: All models use same feature definitions - Performance: Features computed once, cached - Governance: Track feature lineage, versioning

Priority: MEDIUM (nice-to-have, not critical for initial deployment)

---

6. Vertex AI Model Monitoring

What We Missed: Automated detection when models degrade over time (data drift, concept drift).

Problem: - Model trained on pre-COVID call patterns - Post-COVID, customer behavior changes - Model accuracy drops from 85% → 65% silently

Solution: Model Monitoring

## Enable monitoring during endpoint deployment

endpoint = aiplatform.Endpoint.create(
    display_name="wxcc-churn-model",
    monitoring_config={
        "alert_config": {
            "email_alert_config": {"user_emails": ["mlops@abhavtech.com"]}
        },
        "drift_detection_config": {
            "drift_thresholds": {"customer_tenure": 0.05}  # Alert if >5% drift
        },
        "skew_detection_config": {
            "skew_thresholds": {"sentiment_score": 0.1}
        }
    }
)

Monitors: - Training-Serving Skew: Input data distribution changes - Prediction Drift: Output distribution changes (e.g., suddenly predicting 80% churn vs baseline 20%) - Feature Attribution Drift: Which features model relies on changes

Priority: HIGH (prevent silent model degradation)

---

Medium Priority - Operational Enhancements

7. Cloud Composer (Apache Airflow)

What We Missed: Complex workflow orchestration beyond Cloud Scheduler.

Use Case:

Daily WxCC ML Pipeline:
1. Export CDR from WxCC → BigQuery (5 min)
2. Wait for export to complete
3. Run data quality checks (10 min)
4. If checks pass → Train churn model (2 hours)
5. If accuracy >80% → Deploy to production
6. Send summary email to ML team

Cloud Scheduler can't handle this logic (dependencies, conditionals, retries). Need Airflow.

Priority: MEDIUM (use Cloud Scheduler initially, upgrade to Composer if workflows become complex)

---

8. Memorystore (Redis)

What We Missed: Real-time caching for agent dashboards.

Problem: - Agent opens dashboard showing "Customer 360 view" - Query: "Get last 20 interactions, CSAT history, churn score" - BigQuery query takes 5 seconds (too slow!)

Solution: Redis Cache

import redis

r = redis.Redis(host='10.200.2.50', port=6379)

## Cache customer data for 5 minutes

customer_data = r.get(f"customer:12345")
if not customer_data:
    customer_data = bigquery_query("SELECT * FROM customers WHERE id=12345")
    r.setex(f"customer:12345", 300, customer_data)  # TTL 5 min

return customer_data  # <50ms response time

Priority: MEDIUM (optimize agent dashboard performance)

---

9. Cloud CDN

What We Missed: If agent dashboards served globally, need CDN for low latency.

Scenario: - Agent in Mumbai accesses dashboard served from us-east4 - Latency: 200ms - With Cloud CDN: 20ms (cached at Mumbai PoP)

Priority: LOW (agents typically in fixed locations, not traveling)

---

10. Cloud Armor (DDoS Protection)

What We Missed: If exposing WxCC analytics APIs to external systems (e.g., Salesforce), need DDoS protection.

Configuration:

## Create Cloud Armor security policy

gcloud compute security-policies create wxcc-api-protection \
  --description "Protect WxCC analytics API"

## Add rate limiting rule

gcloud compute security-policies rules create 1000 \
  --security-policy wxcc-api-protection \
  --expression "true" \
  --action "rate-based-ban" \
  --rate-limit-threshold-count 100 \
  --rate-limit-threshold-interval-sec 60 \
  --ban-duration-sec 600

Priority: MEDIUM (if APIs are public-facing)

---

Low Priority - Future Enhancements

11. Recommendations AI

• Product/service recommendations during customer calls
• "Customer bought Product A, suggest Product B"
• Priority: LOW (business-driven feature)

12. Document AI

• OCR for scanned documents (if WxCC integrates with ticketing)
• Extract data from customer-uploaded forms
• Priority: LOW (not in current scope)

13. Cloud Healthcare API

• Only if WxCC handles medical support lines (HIPAA compliance)
• Priority: N/A (not applicable to Abhavtech)

---