CML Insights - ML Platform & Products

Overview

As Machine Learning Engineering Lead at CML Insights, I architect and implement end-to-end ML solutions spanning multiple products in educational and financial domains. My role encompasses infrastructure design, MLOps implementation, database architecture, and hands-on development of production ML systems.

Products & Solutions

CML Insights App

Core analytics platform providing ML-powered insights for educational assessment and intervention.

• Microservices architecture on Kubernetes
• Real-time data processing pipelines
• Custom ML models for predictive analytics
• RESTful APIs with comprehensive authentication

Evidence Hub Ecosystem

Two-part solution for evidence collection and curation:

Evidence Hub App

• Mobile and web data collection system
• Multi-modal evidence capture (text, image, video)
• Offline-first architecture with sync capabilities

Evidence Hub Curator App

• Content management and review workflows
• ML-assisted tagging and categorization
• Admin dashboards for quality control

Fair Appraisal App

ML-driven appraisal and evaluation system:

• Automated scoring algorithms
• Bias detection and fairness metrics
• Explainable AI for decision transparency

Technical Architecture

MLOps Infrastructure

Built comprehensive MLOps pipelines enabling rapid experimentation and deployment:

Orchestration

• Kubeflow Pipelines for ML workflows
• MLflow for experiment tracking and model registry
• Dagster for data pipeline orchestration
• Custom automation for model versioning and deployment

Infrastructure as Code

• Terraform modules for AWS resource provisioning
• Kustomize for Kubernetes configuration management
• GitOps workflows with ArgoCD
• Automated environment provisioning (dev/staging/prod)

Monitoring & Observability

• Grafana dashboards for system and ML metrics
• Prometheus for metrics collection
• Loki for log aggregation
• Alert Manager for proactive incident response

Authentication & API Gateway

• Keycloak for identity and access management
• Kong Gateway for API routing and rate limiting
• OAuth 2.0 and OIDC implementations
• Role-based access control (RBAC)

Database Design

Designed normalized schemas optimizing for:

• High-throughput ML feature access
• Transaction consistency for application data
• Efficient querying for analytics workloads
• PostgreSQL with read replicas and connection pooling

ML Capabilities

• Scikit-learn for classical ML models
• PyTorch for deep learning applications
• Dask for distributed data processing
• Integration with OpenAI API for LLM features
• Hugging Face model deployment

Notable Client Projects

Kids Read Now

Educational literacy program serving thousands of students:

• ML models predicting reading progress and intervention needs
• Data pipelines processing reading assessments
• Dashboards for educators and administrators
• Scalable infrastructure handling peak loads

JG Wentworth

Financial services ML solutions:

• Risk assessment models
• Document processing with NLP
• Secure data handling complying with financial regulations

Technical Challenges & Solutions

Scalability

Challenge: Handle variable loads across multiple products
Solution: Kubernetes HPA with custom metrics; microservices isolation; asynchronous processing with message queues

Deployment Velocity

Challenge: Reduce time from model training to production
Solution: Automated CI/CD pipelines; containerized deployments; feature flags for safe rollouts; comprehensive testing automation

Data Privacy & Security

Challenge: Handle sensitive educational and financial data
Solution: Encryption at rest and in transit; audit logging; compliance with FERPA and SOC 2; regular security audits

Cost Optimization

Challenge: Manage infrastructure costs while maintaining performance
Solution: Right-sizing resources; spot instances for batch workloads; intelligent caching; query optimization

Impact

• Scale: Serving 4 production applications with thousands of daily active users
• Reliability: Maintained 99.5%+ uptime across all services
• Velocity: Reduced feature deployment time from weeks to days
• Team Growth: Established MLOps best practices adopted across engineering teams

Technology Stack

Languages: Python, SQL, Shell scripting
ML/Data: PyTorch, Scikit-learn, Pandas, Dask, NumPy
MLOps: Kubeflow, MLflow, Dagster
Infrastructure: Kubernetes, Docker, Terraform, Kustomize
Cloud: AWS (EKS, S3, RDS, EC2, Lambda)
CI/CD: ArgoCD, GitHub Actions
Monitoring: Grafana, Prometheus, Loki
Security: Keycloak, Kong Gateway
Databases: PostgreSQL, Redis
APIs: OpenAI, Hugging Face