Skip to content
EduProMentor
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

  1. 01Packaging & serving

    3 topics
    • BentoML/Ray Serve
    • vLLM/TGI
    • gRPC vs REST
  2. 02Pipelines

    3 topics
    • Kubeflow/Airflow
    • Feature stores
    • Data contracts
  3. 03Observability

    3 topics
    • Traces, metrics, evals
    • Drift detection
    • Canary + shadow
  4. 04Cost & performance

    3 topics
    • Quantization
    • Batching
    • Caching

Prerequisites

  • Working ML experience
  • Docker + basic Kubernetes
₹59,999
Next batch · May 24, 2026
Enroll