Abbott

Alinity

Advanced Analytics for 27,000+ Diagnostic Devices

Problem
Legacy on-prem ML pipeline couldn't scale for 27,000 devices; 6-month deployment cycles; FDA audit concerns
Solution
HIPAA-compliant AWS migration with zero audit findings, reducing deployment time from 6 months to 3 weeks
Abbott Alinity Analytics Platform
27K+Devices connected
0FDA audit findings
88%Faster deployments
99.99%Uptime achieved

27,000 devices. Legacy infrastructure. FDA watching.

Abbott's Alinity diagnostic platform connects 27,000+ devices globally. The existing on-prem infrastructure was hitting limits with 6-month deployment cycles and incomplete audit trails.

6 modeployment cycles
27Kdevices to support
FDAinspection pending
Legacy Infrastructure Risks
Data Silos
27K devices across disconnected systems
Compliance Gap
No audit trail for model changes
Manual Ops
6-month deployment cycles
Single Point
Critical scripts on individual laptops

Migration Discovery Timeline

Three-month discovery phase.

W1W2W3W4W5W6W7W8W9W10W11W12W13W14W15W16W17DiscoveryArchitectureEngineeringEnablementLegacy System AssessmentLegacy System Assessment Week 1 - 6 Progress: 100%100%HIPAA Gap AnalysisHIPAA Gap Analysis Week 5 - 8 Progress: 100%100%Data Pipeline AuditData Pipeline Audit Week 7 - 10 Progress: 100%100%AWS Architecture DesignAWS Architecture Design Week 9 - 12 Progress: 100%100%Security ReviewSecurity Review Week 11 - 14 Progress: 100%100%Migration PlanningMigration Planning Week 13 - 15 Progress: 100%100%
Discovery(2)
Architecture(2)
Compliance(2)

Legacy vs. Cloud Architecture

Side-by-side comparison.

Infrastructure Transformation

Before

Legacy On-Premises

Constrained infrastructure with compliance gaps

▧Physical servers (EOL)
◉Manual deployments
▤Incomplete audit logs
6 months
Deployment Time
99.5%
Uptime
Pain Points
✕6-month deployment cycles
✕Manual validation prone to error
✕Incomplete audit trails
Migration
After

GxP-Compliant AWS

Scalable, auditable, FDA-ready infrastructure

○AWS SageMaker + EKS
⟳GitOps CI/CD
▥CloudTrail audit logs
3 weeks
Deployment Time
99.99%
Uptime
Benefits
✓Automated compliance validation
✓Complete audit trail
✓Multi-region DR
Key Transformation Metrics
MetricBeforeAfterImprovement
Deployment Time6 months3 weeks88% faster
Platform Uptime99.5%99.99%10x fewer outages
FDA Audit Findings12 gaps0 findings100% compliant

AWS architecture built for FDA inspection.

Designed a 7-layer architecture on AWS that meets GxP requirements for medical device software.

Compliance: FDA 21 CFR Part 11, HIPAA, SOC 2
Scale: 150K+ events/second peak throughput
Hybrid Cloud Architecture
AWS
• SageMaker
• S3
• RDS
• Lambda
Azure
• ML Workspace
• Data Factory
• Synapse
• AKS

GxP-Compliant AWS Architecture

Seven-layer architecture for FDA audit readiness.

GxP-Compliant AWS Architecture

HIPAA-compliant ML pipeline for 27,000+ diagnostic devices

1
Device Ingestion Layer
▼
2
Streaming Layer
▼
3
ML Processing Layer
▼
4
Data Lake
▼
5
Analytics Layer
▼
6
Governance Layer
▼
7
Network Layer
▼

Zero-downtime migration. Zero audit findings.

Migrated the entire ML pipeline to AWS without disrupting device connectivity using blue-green deployment.

99.99%uptime SLA
150Kevents/sec peak
<100msp95 latency
Production SLOs
Deployment Time6 mo → 3 wk
Uptime95% → 99.9%
FDA Findings3 → 0
Data Scientists12 → 45

Platform Service Health

Real-time monitoring across all pipeline services.

Device Gateway● Healthy
Uptime
99.99%
p95 Latency
85ms
Error Rate
0.01%
ML Pipeline● Healthy
Uptime
99.95%
p95 Latency
350ms
Error Rate
0.02%
Data Lake● Healthy
Uptime
99.999%
p95 Latency
50ms
Error Rate
0.001%
Analytics API● Healthy
Uptime
99.9%
p95 Latency
250ms
Error Rate
0.03%
Audit Service● Healthy
Uptime
99.999%
p95 Latency
30ms
Error Rate
0.001%
API Gateway Latency (24h)
020040060000:00
p50
p95
p99
Aggregate Metrics
Avg Uptime
99.97%
Avg Error Rate
0.01%
Total Throughput
903.5K
req/min
Active Incidents
0
services affected
Active Alerts
✓ All systems operational

From 6 months to 3 weeks.

Established CI/CD pipelines with automated compliance checks. Trained 45+ data scientists on the new platform.

Before

  • ✕ 6-month deployments
  • ✕ Manual validation
  • ✕ Incomplete audit trails

After

  • ✓ 3-week deployments
  • ✓ Automated validation
  • ✓ Complete audit trails
MLOps Adoption
45
Data Scientists
100%
CI/CD Coverage
MLflow
Experiment Tracking
0
Manual Deploys

Deployment Time Comparison

Before and after migration in days.

$0M$83M$165M$248M$330MBefore - deployment: $180MBefore - validation: $90MBefore - rollback: $60MBefore$330MAfter - deployment: $21MAfter - validation: $14MAfter - rollback: $7MAfter$42M
deployment
validation
rollback

The Result

The FDA inspection came and went with zero findings. Model deployment time dropped 88%. The platform now handles 150K+ events/sec with 99.99% uptime.

6 months→3 weeksDeployment Time
Audit gaps→0 findingsFDA Inspection
99.5%→99.99%Platform Uptime
Migration Outcomes
0
FDA Audit Findings
87%
Faster Deploy
3.8×
Team Scale

Technology Stack

Core technologies powering the GxP-compliant ML platform.

ML Platform
SageMaker
Model training
AWS
Infrastructure
Compliance
HIPAA
PHI protection
FDA
21 CFR Part 11