Program Overview
Deploy and Monitor Models Like the Best
Deploying an ML model is just the beginning — keeping it performing reliably in production is the real challenge. This hands-on program covers model optimization (quantization, pruning, ONNX), serving infrastructure (Triton, Ray Serve, FastAPI), and comprehensive monitoring (data drift, prediction drift, performance degradation). Updated for 2025 with LLM serving patterns, GPU optimization, and real-time inference observability.
Model Optimization
Reduce model size and latency with quantization, pruning, and ONNX conversion techniques.
Production Serving
Deploy models with Triton, Ray Serve, and FastAPI for batch and real-time inference.
Comprehensive Monitoring
Detect data drift, model degradation, and performance issues before they impact users.
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
AI Engineers
Engineers deploying and maintaining ML models in production environments.
ML Engineers
Engineers focused on model serving infrastructure and performance optimization.
MLOps Practitioners
Practitioners building automated deployment and monitoring pipelines.
What You'll Walk Away With
Tangible deliverables and capabilities for your organization
Optimized Model Deployment
A production-deployed, optimized ML model with quantization and inference optimization.
Monitoring Dashboard
Comprehensive monitoring setup tracking data drift, model performance, and latency.
Operations Playbook
Production runbooks for model updates, rollbacks, and incident response.
Deployment Certification
Certification validating model deployment and monitoring expertise.