DevOps&MLOpsPlatform
End-to-end DevOps and MLOps pipelines — CI/CD, containerized deployments, model versioning, monitoring, and automated retraining workflows for private production environments.
Client
Private Client
Timeline
Infrastructure setup & automation
Type
CI/CD & Model Lifecycle
Delivery
Private
CI/CD
Automated
MLOps
Model Lifecycle
Private
Infra
01Overview
A full DevOps and MLOps foundation for shipping and maintaining ML-powered applications — covering build automation, containerized deployments, experiment tracking, model registry, and production monitoring for private client infrastructure.
02The Problem & The Fix
The Challenge
ML models and application code were deployed manually with no standardized pipelines, version control for models, or observability. Releases were slow, rollbacks were risky, and retraining cycles lacked automation.
Our Solution
We implemented CI/CD for application and model artifacts, containerized services, experiment tracking, model promotion workflows, and monitoring hooks — giving the client a repeatable path from training to production.
03Our Approach
Pipeline Design
Mapped build, test, and deploy stages for both app code and ML model artifacts.
MLOps Tooling
Set up experiment tracking, model registry, and versioned deployments with rollback support.
Observability
Added health checks, logging, and monitoring for production model and API performance.
04What We Delivered
Key Deliverables
- CI/CD pipelines for app and ML workloads
- Containerized deployment templates
- Model versioning & registry setup
- Monitoring and alerting configuration
- Runbooks for release and rollback
Features Built
- Automated build, test, and deploy pipelines
- Docker-based service packaging and orchestration
- ML experiment tracking and model version promotion
- Production monitoring with drift and performance alerts
- Private infrastructure — internal ops only
05The Impact
Faster, safer releases with automated deployment pipelines
Traceable model versions from experiment to production
Reduced manual ops overhead for ML and app teams
Scalable foundation for ongoing model retraining cycles
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