Client
Private Client
Timeline
Infrastructure setup & automation
Built by
thehsquares
Project Overview
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.
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.
Our 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.
Services 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
Key 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
Impact & Results
- 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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