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Eliminate Model Drift and Manual Deployments with Automated MLOps on AWS

A leading healthcare provider relied on machine learning to predict wound-healing time and support clinical decision-making. However, manual deployments, outdated models, and lack of monitoring were slowing operations and reducing accuracy. DataOptix implemented an automated MLOps solution on AWS SageMaker, enabling continuous retraining, reliable deployments, and real-time performance visibility.

Challenge

No automated retraining, manual deployment, and lack of monitoring caused outdated, unreliable models.

Solution

Implemented full MLOps automation with retraining, monitoring, A/B testing, and controlled releases.

Results

Achieved faster, reliable, and continuously improving model performance for better clinical decisions.

Table of Contents

About the Client

A healthcare company focused on treating patients with critical injuries and chronic wounds. Their machine learning model predicts estimated healing timelines and helps clinicians deliver timely, data-backed treatment plans.

The Business Problem

Although the client had a working ML model, it faced several operational challenges:

  • Model performance degraded as new patient data arrived
  • Manual deployments caused errors and downtime
  • No visibility into model drift or accuracy drops
  • No automated retraining or testing of updated models
  • No environment separation for Dev, Staging, and Production

The result: delayed model updates, inconsistent insights, and slower clinical support.

Core Challenges Identified

ChallengeImpact
No automated retrainingModel drift and outdated predictions
Manual deployment workflowSlow, error-prone releases
Lack of monitoring & alertsIssues detected only after user complaints
No A/B or shadow testingHigh risk in promoting new models
Single-environment setupNo controlled release process

Our Solution: Automated MLOps on AWS SageMaker

DataOptix built an end-to-end CI/CD and MLOps pipeline designed for reliability, version control, and continuous improvement.

Key Capabilities Delivered

  • Automated data cleaning, transformation, and ingestion for training
  • CI/CD pipeline for retraining, evaluation, and deployment
  • Multi-environment model promotion (Dev → Staging → Prod)
  • A/B testing and rollback support for safer releases
  • Continuous monitoring, logging, and drift detection
  • Trigger-based retraining on new data or code changes

Architecture Overview (High-Level Workflow)

  1. New data arrives in S3 → retraining pipeline auto-triggers
  2. AWS SageMaker trains and evaluates updated model
  3. CodeBuild + CloudFormation automate CI/CD steps
  4. API Gateway + SageMaker Endpoints serve predictions
  5. Monitoring tracks drift, latency, and accuracy
  6. A/B testing validates before full rollout
  7. Production deployment occurs only if KPIs meet the defined criteria

Tools & Technologies Used

  • AWS SageMaker Pipelines
  • AWS CodeBuild, CloudFormation, API Gateway, S3
  • Python / CI-CD scripts

Key Business Outcomes

ResultOutcome
100% automated ML lifecycleZero manual intervention post-deployment
Faster deploymentReleases in minutes, not days
Improved model accuracyContinuous learning prevents drift
Higher model reliabilityMonitoring + rollback ensures stability
Better clinical decisionsDoctors get timely, accurate predictions

The healthcare team now has a stable, self-improving ML ecosystem supporting patient care with confidence and speed.

Why DataOptix

  • Proven expertise in MLOps, Cloud, Data Engineering, and ML automation
  • Experience in healthcare-grade reliability and compliance-focused workflows
  • Ability to build scalable, production-ready ML systems on AWS
  • Advisory approach with focus on long-term maintainability and cost efficiency