Phase 06 of the Data Science Lifecycle

From Model
to Production System

A model only creates value when it actively supports decisions in production.

Back to Overview Phase 05 Discuss a project

ML deployment

From Prototype to Production

Deployment is a continuous process. We ensure reliable, maintainable operations.

Infrastructure

Production deployment - scalable and highly available.

Monitoring

Continuous monitoring with automated alerts.

Continuous Improvement

Processes for model updates as requirements change.

ML deploymentautomated · observable · self-healing

Code Push

Step 1

Test & Validate

Step 2

Build Container

Step 3

Staging

Step 4

Canary

Step 5

Production

Step 6

Production Monitoring Dashboard

LATENCY (ms)

avg: 42 ms ↓

PREDICTIONS / HOUR

12.4K total ↑

SYSTEM STATUS

Model HealthHEALTHY

Data DriftNORMAL

Uptime99.97%

Last Retrain3 DAYS AGO

Data science project approach

Our Approach

Productionisation

Preparing for production use with interfaces and quality checks.

Automated Release

Traceable deployment process with tests and rollback options.

Monitoring & Alerting

Early detection of changes in model performance and data quality.

Operations Handover

Knowledge transfer, documentation, and process establishment for your team.

Data science deliverables

Typical Deliverables

Production-ready ML system

Automated deployment process

Monitoring and observability concept

Operations documentation and knowledge transfer