ML Phase 4
MLOps &
Deployment
Taking models out of the lab. Production deployment, security hardening, A/B testing, and continuous operational monitoring.
Operational Excellence
Production Engineering
Building the model is only half the battle. This demands a robust Production Environment. We implement Multi-Zone Resiliency, Auto-Scaling Container Fleets, and Resource Rightsizing to ensure your ML solution is performant, fault-tolerant, and cost-effective.
Multi-Zone Architecture
Global Load Balancer
Availability Zone A
Instance A1
Instance A2
Availability Zone B
Instance B1
Instance B2
Fault Tolerance Strategy: We deploy across multiple physical locations (Zones). If Zone A fails, the Load Balancer instantly reroutes traffic to Zone B, and Auto-Scaling adds capacity to handle the load.
Security & Governance
Defense in Depth
Security is not an add-on; it's architectural. We implement Zero-Trust Principles across three pillars: Identity (Who can act?), Network (Where can they act?), and Data (What can they see?). This ensures compliance with enterprise standards like ISO 27001 and SOC2.
Access Policy Simulator
Active Policy Definition
Can experiment and train, but cannot push to production or see sensitive user data.
Read Training Data
Train Model
Deploy to Prod
View Decrypted PII
Operationalization
Deploy & Operationalize
Deployment is not the finish line. This phase focuses on the Operational Lifecycle: exposing models via secure endpoints, validating performance with A/B Testing, and building Self-Healing Pipelines that automatically retrain when data drift is detected.
API Endpoint Tester
POST /v1/predict
Request Body (JSON)
{
"input": {
"features": [
0.5,
2.1,
-1.5,
0
],
"context": "user_transaction"
}
}Response
Waiting for response...
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