Program Overview
Operationalize Machine Learning at Scale
Only 54% of ML models make it to production (Gartner 2024). MLOps bridges the gap between model development and reliable production deployment. This project-led program covers the entire MLOps lifecycle — pipeline design, experiment management, model versioning, CI/CD for ML, serving infrastructure, and drift monitoring. Updated for 2025 with LLMOps integration, feature platform design, and ML platform architecture patterns used by leading tech companies.
Project-Led Learning
Build a complete MLOps pipeline end-to-end — from feature store to production serving and monitoring.
Platform Architecture
Learn ML platform design patterns used by Uber, Netflix, and Spotify for production ML.
Automated Lifecycle
Implement automated training, validation, deployment, and retraining triggers.
Curriculum
Program Curriculum
Structured learning path designed for measurable outcomes
Skills You'll Build
Tools & Technologies
Who Should Attend
Designed for professionals ready to lead and execute real change
ML Engineers
Engineers responsible for deploying and maintaining ML models in production environments.
Data Scientists
Data scientists looking to operationalize their models and build production skills.
Platform Engineers
Engineers building ML platform infrastructure for their organizations.
What You'll Walk Away With
Tangible deliverables and capabilities for your organization
Production MLOps Pipeline
A complete, deployed MLOps pipeline from data ingestion to model serving and monitoring.
Monitoring Dashboard
Real-time model performance and data drift monitoring with automated alerts.
Platform Architecture Doc
ML platform architecture documentation with design decisions and operational runbooks.
MLOps Certification
Certification validating end-to-end MLOps practitioner capabilities.