Deploy a Model on GPUs — Live in 60 Minutes
Join Marcus Chen (Principal Platform Engineer · Databricks) for a free live session.
Marcus Chen
Principal Platform Engineer · Databricks
⭐ 4.9 / 5
You've been looking to learn AI infrastructure — not start from ML basics.
Built for engineers who already own Kubernetes: six weeks across GPU Operator, vLLM, KServe, Triton, KubeFlow, and MLflow — the full AI infrastructure stack taught from the infra side. No ML prerequisites, no 15-course sprawl — just what your job description actually requires.
What You'll Learn
AI Infrastructure Blueprint
Design a GPU cluster architecture with Terraform node pools — covering inference economics, resource management, and cost trade-offs across cloud environments.
GPU Orchestration Framework
Deploy NVIDIA GPU Operators on Kubernetes with MIG partitioning and multi-tenant scheduling — verified running in a live lab cluster.
LLM Inference System
Deploy Mistral-7B behind vLLM and KServe, optimized with Triton — benchmarked for P50/P99 latency and tokens/sec against production targets.
End-to-End MLOps Pipeline
Build a KubeFlow pipeline wired to MLflow, with Evidently drift detection and automated retraining triggers — production-ready from day one.
Who Is This For?
This course is designed for:
Cloud Engineers Expanding into AI
Who need to incorporate GPU orchestration and MLOps into their cloud environments to meet the demands of new AI-oriented projects.
DevOps Professionals Facing AI Workloads
Who are tasked with deploying and managing AI models in production environments but lack the specific skills for GPU and LLM operations.
ML Engineers Taking Models to Production
Who can train and fine-tune models but need the infrastructure layer — GPU scheduling, inference serving, and MLOps pipelines — to ship to production without depending on a separate platform team.
Course Outline
6 weeks · 3 sessions per week
Projects You'll Ship
Leave with real work to show, not just a certificate.
AI Infrastructure Architecture Plan
A detailed architecture plan for AI infrastructure that includes GPU cluster configurations and inference economics. This plan will serve as a foundational document for deploying AI workloads at scale.
LLM Inference Deployment Manual
A comprehensive manual for deploying and optimizing LLM inference systems using KServe and Triton, focusing on real-world performance metrics. A reusable guide for deploying various LLMs in production.
MLOps Pipeline Configuration Document
A detailed pipeline configuration document using KubeFlow and Argo, covering model versioning and drift detection. A blueprint for implementing robust MLOps practices in any organization.
Your Instructors
Marcus Chen
Principal Platform Engineer · Databricks
⭐ 4.9 / 5
Marcus Chen is the Principal Platform Engineer for AI Infrastructure at Databricks, where he runs GPU cluster operations across 2,000+ nodes on AWS and Azure and owns the LLM inference platform serving production workloads. Before Databricks, he spent five years as a Senior SRE at Google Cloud. He teaches from the infra side — not the ML side.
What Students Say
⭐⭐⭐⭐⭐
"The GPU orchestration knowledge Marcus shared was instrumental in optimizing our AI cluster management. Implementing it saved us substantial costs."
Samantha Lee
Cloud Engineer · Cloudflare
⭐⭐⭐⭐⭐
"Deploying LLMs with KServe and Triton was a game-changer for our team. The real-world exercises made it easy to apply right away."
Daniel Hughes
DevOps Engineer · Rippling
⭐⭐⭐⭐⭐
"The MLOps pipeline we built during Week 4 is now the backbone of our AI operations. It's streamlined our deployment process significantly."
Jennifer Tran
SRE · Brex
Course Schedule
All sessions are instructor-led and live. Recordings available within 24 hours.
SUNDAY
9:00 AM PDT
Live ClassIn-depth exploration of AI infrastructure architecture and GPU orchestration techniques.
WEDNESDAY
6:00 PM PDT
Lab SessionHands-on lab to apply weekly frameworks and tools, addressing any blockers.
THURSDAY
6:00 PM PDT
Build & ShipExecute and peer review deliverables, focusing on real-world applicability.
Frequently Asked Questions
Related reading
The JD-backed research behind this course — from Dexity Intel.
The 2026 AI Infrastructure Stack: A Practical Guide →How far do you want to go?
Start free to experience our offering, choose the program length you would want to commit to.
You build. Nobody demos at you.
Every session is follow-along — you build the thing yourself while a practitioner works beside you. That is why the hours look long: they are yours to build in, with an expert on hand to guide you. None of it is a traditional lecture.
In the session, you'll: