Still getting paged because a model silently started returning garbage? Most teams can deploy a model once. This is the course that teaches you to keep it running when traffic spikes, data drifts, and things break at 2am — reliably, not by luck.
Learn Kubernetes, model serving at scale, observability, and incident response the way production ML platform teams actually run them — building towards a capstone that mirrors a real on-call rotation.
By the time you finish this course, you will have deployed a model on Kubernetes, configured autoscaling for it, instrumented it with metrics and dashboards, and run a simulated incident response using a runbook you wrote yourself — the same operational rigor production ML platform teams run on. This course is how you get there in 8 weeks, building directly on MLOps Foundations rather than re-teaching it.
Syllabus
Phase 1: Kubernetes & Production Serving (Weeks 1–4)
Modules
From Foundations to Production & Kubernetes Basics
Deploying & Serving Models on Kubernetes
Scaling ML Workloads
Modern Serving Frameworks
Feature Consistency & Applied Lab
Phase 2: Observability, Incident Response & Capstone (Weeks 5–8)
Modules
Observability Fundamentals (Prometheus & Grafana)
Data & Model Drift, SLOs & Alerting
Incident Response for ML Systems
Capstone Project
Full session-by-session breakdown (all 24 sessions, 2-hour format) is in the downloadable curriculum PDF linked from the hero.
Outcome
By the end of this course, you will be able to:
- Deploy and serve ML models on Kubernetes using Deployments, Services, and Ingress
- Configure autoscaling and understand GPU-aware scheduling for inference workloads
- Apply safer release patterns: canary, blue-green, and shadow deployments
- Instrument a serving system with logging, metrics, and distributed tracing
- Build monitoring dashboards with Prometheus and Grafana
- Detect data and model drift and design actionable alerts and SLOs
- Run an incident response using an on-call mindset and a runbook you wrote
- Deliver a capstone project that operates a real ML system end-to-end
- Career roles you'll be ready for: MLOps Engineer, ML Platform Engineer, Site Reliability Engineer (ML systems)
Tools
Kubernetes
Docker
Prometheus
Grafana
KServe
Python
Git & GitHub
MLflow
Who Should Enrol
Already through MLOps Foundations?
Phase 1 skips straight past Git/DVC/MLflow basics into Kubernetes. No repeated fundamentals.
MLOps engineer who's only deployed to a single VM or a managed endpoint?
Every concept is taught with a working lab — you'll leave with a model that's actually autoscaled and monitored, not just “up.”
DevOps/SRE engineer already comfortable with Kubernetes?
Phase 1 moves fast through Kubernetes fundamentals — you'll be deploying and scaling real serving workloads by Week 2.
Considering the full MLOps Engineering track?
This is Course 2 of 2. Complete MLOps Foundations first if you haven't — this course builds directly on it.
Market Growth
Average MLOps Salary
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Senior MLOps Salary
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Hiring Growth
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Companies Hiring MLOps Talent
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FAQs
MLOps engineers and MLOps Foundations graduates who want to operate ML systems at production scale, not just deploy them once.
It’s recommended. This course assumes Git, DVC, MLflow, Docker, and CI/CD fundamentals, and builds directly on top of that.
No. Phase 1 teaches Kubernetes fundamentals from the ground up, then moves quickly into ML-specific serving and scaling patterns.
A capstone project where you deploy, scale, and monitor a real ML system on Kubernetes, then simulate and respond to an incident using a runbook you write yourself.
AI & ML Pathway teaches you to build models. MLOps Foundations teaches you to version, track, and deploy one reliably. Production MLOps teaches you to run that model at scale, in production, and handle it when it breaks.