Machine Learning.
Machine Learning
Machine learning is how software learns from data. Instead of writing rules that tell a program what to do, you give it examples and it figures out the patterns. A spam filter doesn't have a list of spam words. It learned what spam looks like from millions of emails.
Churn Prediction: Classical ML Full Loop
CLIENT · EMEKA OKAFOR
Artifact Creation Pipeline: Churn Prediction Phase 2
CLIENT · EMEKA OKAFOR
Infrastructure Foundation: Serving and Experiment Tracking
CLIENT · EMEKA OKAFOR
Data Leakage and PyTorch Training
CLIENT · MARIANNE VELASQUEZ
Docker Containerization and Production Pipelines
CLIENT · MARIANNE VELASQUEZ
Pipelines, Transfer Learning, and Fairness
CLIENT · PRIYA KRISHNAMURTHY
CI/CD Pipelines and Drift Detection
CLIENT · PRIYA KRISHNAMURTHY
Feature Versioning and Production Monitoring
CLIENT · MAX EHRLICH
Cloud Deployment and Multi-Service Orchestration
CLIENT · MAX EHRLICH
MCP Connection and Cost Monitoring
CLIENT · AMINA BENALI
Project Memory and Evaluation Workflow Encoding
CLIENT · DUC TRAN
Production Features, SHAP, and Training-Serving Skew
CLIENT · MEI-LING TAN
Classical ML Capstone: Model Registry, Automated Retraining, and A/B Testing
CLIENT · MEI-LING TAN
Semantic Search: Embeddings, Retrieval, and Local LLM Serving
CLIENT · DIEGO FUENTES
RAG Pipelines: Hybrid Retrieval, Generation, and Groundedness Verification
CLIENT · YUKI NAKAMURA
Bridging Embeddings and Classical ML with Delegated Agents
CLIENT · KEREM YILMAZ
Hybrid Production Systems: Routing a Classical Model and an LLM Under Constraints
CLIENT · ANA BEATRIZ COSTA
Cloud RAG: Multi-Index Retrieval, Life-Safety Guardrails, and Authored AI Infrastructure
CLIENT · TARIQ AL-RASHID
Fine-tuning an Open-Weight Model on a Tight Budget
CLIENT · AMA MENSAH
Approach Selection Across Modelling Paradigms
CLIENT · WILLEM DE VRIES
Classical ML Production Architecture
CLIENT · SHU-FEN LIN
Retrieval Production Architecture and Monitoring
CLIENT · CARLOS QUISPE
AI Development Infrastructure: Design, Prove, and Hand Off
CLIENT · AROHA MITCHELL
Auditing an Inherited ML System: Diagnose, Fix, Hand Over
CLIENT · JAMES HOLLOWAY
Surviving a Tool Deprecation: Evaluate, Migrate, Make the Next One Painless
CLIENT · MARIA SANTOS
Capstone: Architecting a Complete ML System from One Email
CLIENT · JEAN-PIERRE HABIMANA