How JSN Cloud transformed a Series B AI startup's manual deployment processes into automated CI/CD pipelines, achieving 10x deployment speed with 95% fewer incidents while scaling from 50 to 500+ engineers.
A rapidly growing AI startup that had just closed their Series B funding and was scaling aggressively to meet enterprise customer demands. Their engineering team grew from 50 to 250+ engineers in 18 months, but their infrastructure and deployment processes hadn't scaled accordingly, creating significant bottlenecks.
The startup's rapid growth had outpaced their infrastructure capabilities. What worked for a 50-person engineering team was completely inadequate for 250+ developers trying to deploy multiple times per day. Manual processes that took hours were blocking critical customer releases and preventing the team from scaling effectively.
With enterprise customers expecting 99.9% uptime and rapid feature delivery, the company needed to transform their entire development and deployment infrastructure to support their growth trajectory while maintaining the agility that made them successful.
Deployments required 4-8 hours of manual work, involving multiple teams and prone to human error. Only 2-3 deployments possible per week, severely limiting feature velocity and customer responsiveness for a fast-moving AI startup.
No standardized deployment processes across 150+ microservices, inconsistent environments between development and production, and no automated scaling for ML workloads causing frequent outages during traffic spikes.
Engineers spending 60% of time on infrastructure tasks instead of product development, new hire onboarding taking 3+ weeks, and frequent context switching between coding and deployment management reducing innovation velocity.
95% of production incidents caused by deployment issues, no automated testing in CI/CD pipeline, and manual rollback processes taking hours, damaging customer trust and requiring expensive emergency response procedures.
No standardized ML model versioning or deployment pipeline, manual A/B testing setup, and inconsistent model performance monitoring across environments, preventing rapid iteration on AI capabilities.
JSN Cloud designed and implemented a comprehensive DevOps transformation strategy optimized for AI/ML workloads. Our approach focused on automation, standardization, and developer experience while maintaining the flexibility needed for rapid experimentation and iteration.
Automated pipeline from data ingestion to model deployment with comprehensive validation and testing at each stage.
| Stage | Process | Automation | Validation |
|---|---|---|---|
| Data Ingestion | Automated data collection and preprocessing | Apache Airflow DAGs | Data quality checks |
| Feature Engineering | Feature extraction and transformation | Feast feature store integration | Feature drift detection |
| Model Training | Hyperparameter tuning and training | Kubeflow pipeline execution | Model performance metrics |
| Model Validation | A/B testing and performance comparison | Automated testing framework | Statistical significance testing |
| Model Deployment | Production deployment with monitoring | Seldon Core serving platform | Real-time performance monitoring |
Comprehensive monitoring system tracking model performance, data drift, and business metrics with automated alerting and remediation.
The DevOps transformation enabled the startup to scale their engineering team from 180 to 500+ developers without losing productivity or deployment velocity, maintaining startup agility at enterprise scale.
"JSN Cloud transformed our entire engineering organization. We went from being constrained by infrastructure to being limited only by our imagination. The DevOps transformation was the foundation that enabled our hypergrowth."
Ready to scale your engineering team and deployment velocity? Learn how JSN Cloud can transform your DevOps capabilities for rapid growth.