Case Studies

Challenge

A fintech client needed to deploy machine learning models for algorithmic trading with sub-second latency requirements and 24/7 reliability.

Solution

Built end-to-end MLOps pipeline with:

• Real-time feature engineering pipeline processing market data
• Automated model training and backtesting framework
• Low-latency inference API with FastAPI
• Comprehensive monitoring and alerting system
• Automated model retraining based on performance metrics

Tech Stack

Results

• 99.99% uptime achieved
• Sub-100ms inference latency
• 40% improvement in model deployment speed
• Automated retraining reduced manual intervention by 90%

Business Impact

Enabled the client to deploy new trading strategies 10x faster while maintaining strict risk controls and regulatory compliance.

Challenge

Deploy time-series forecasting models for cryptocurrency markets with high volatility and 24/7 operation requirements.

Solution

• Multi-model ensemble approach with LSTM and XGBoost
• Real-time data ingestion from multiple exchanges
• Automated feature engineering pipeline
• Model drift detection and automated retraining
• Scalable inference on AWS EKS

Tech Stack

Results

• Processing 1M+ predictions per day
• 25% improvement in forecast accuracy
• Automated drift detection preventing model degradation
• 60% reduction in infrastructure costs through optimization

Challenge

Healthcare organization needed reproducible ML workflows with full audit trails for regulatory compliance.

Solution

• Orchestrated ML pipelines with Apache Airflow
• Experiment tracking and model versioning with MLflow
• Automated data validation and quality checks
• HIPAA-compliant infrastructure on Azure
• Complete lineage tracking for audit requirements

Tech Stack

Results

• 100% reproducible experiments
• 70% reduction in model deployment time
• Full regulatory compliance achieved
• Enabled 5 data science teams to collaborate effectively

Challenge

E-commerce company needed to scale ML inference to handle millions of daily predictions with variable load.

Solution

• Kubernetes cluster on AWS EKS with auto-scaling
• GPU node pools for model training
• CPU-optimized inference endpoints
• Blue-green deployment strategy for zero-downtime updates
• Infrastructure as Code with Terraform

Tech Stack

Results

• Handling 10M+ predictions per day
• Auto-scaling reduced costs by 45%
• Zero-downtime deployments achieved
• 99.95% uptime SLA maintained

Challenge

Deploy heart disease prediction model in clinical setting with strict latency and reliability requirements.

Solution

• Production-grade ML API with FastAPI
• Model explainability with SHAP values
• Real-time monitoring and alerting
• HIPAA-compliant deployment on Azure
• Comprehensive testing and validation

Tech Stack

Results

• 85% prediction accuracy maintained in production
• Sub-200ms response time
• Explainable predictions for clinical trust
• Successfully integrated with hospital EHR system