AdvancedLive Online6 weeks
MLOps in Production — Monitoring to Fine-Tuning
Take any model from notebook to production. Pipelines, serving, observability, drift detection, and cost control — the operator's playbook.
★ 4.8 rating1,180 learnersNext batch: May 24, 2026
Next cohort begins in
33
days
14
hours
19
min
46
sec
What you’ll learn
Outcomes, not just content
- ✓Deploy ML models with zero-downtime rollouts
- ✓Build feature stores and serving infrastructure
- ✓Detect data/model drift in production
- ✓Implement cost-aware LLM inference
Curriculum
4 modules · built for depth
01Packaging & serving
3 topics- BentoML/Ray Serve
- vLLM/TGI
- gRPC vs REST
02Pipelines
3 topics- Kubeflow/Airflow
- Feature stores
- Data contracts
03Observability
3 topics- Traces, metrics, evals
- Drift detection
- Canary + shadow
04Cost & performance
3 topics- Quantization
- Batching
- Caching
Prerequisites
- ›Working ML experience
- ›Docker + basic Kubernetes
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