Train your team

AIC-320 · GPU & AI Compute

MLOps & Inference Serving

Get models off a laptop and onto a GPU endpoint that scales, with the pipeline machinery around them.

Practitioner 3 days in person6 half-days online Max 14 in person

Prepares for the NVIDIA NCP-AIO certification.

Who this course is for

Engineers owning the path from trained model to production — experiment tracking, registries, serving stacks and the CI/CD that keeps models improving safely.

Prerequisites

Python and basic ML workflowDocker/Kubernetes basicsAIC-230 recommended

Course outline

Day 1 — Experiment tracking and model management

  • MLflow: tracking, registry, models
  • Weights & Biases: experiments, sweeps, artifacts
  • Reproducibility and hyperparameter tracking
  • Model versioning strategy; artifact storage on S3-compatible (MinIO)
  • Integration with CI/CD

Day 2 — Model serving and inference

  • Triton Inference Server: model repositories, dynamic batching
  • TensorRT optimisation: FP16, INT8, layer fusion
  • TensorRT-LLM: in-flight batching, paged attention, FP8
  • vLLM: PagedAttention, continuous batching
  • ONNX Runtime; REST/gRPC endpoints
  • Multi-model serving, canary deployment, autoscaling, edge

Day 3 — ML pipelines and CI/CD

  • Kubeflow Pipelines and DAG design
  • GitOps for ML: Argo CD, Flux
  • CI/CD for training: automated testing and validation
  • Data validation with Great Expectations
  • Model validation pipelines and retraining triggers
  • Capstone workshop

Hands-on labs

Labs follow the academy model — 35% principles, 20% guided investigation, 45% engineering studio. Every claim you make in a lab is backed by a trace, a counter or a measurement you captured yourself. How we teach

  1. Lab: stand up MLflow with a registry and MinIO artifact store; track and reproduce an experiment exactly
  2. Lab: serve a model with Triton, then optimise it with TensorRT and measure the latency/throughput difference
  3. Lab: deploy an LLM with vLLM or TensorRT-LLM and tune continuous batching for your traffic shape
  4. Lab: build a Kubeflow pipeline with data validation and an automated retraining trigger through GitOps

Capstone project

Ship a model end to end: tracked experiment → registered version → optimised Triton/vLLM deployment behind an autoscaling endpoint → a CI/CD pipeline that validates and promotes the next version, with rollback. Deliver the latency/throughput evidence for each serving decision.

What you leave with

  • A working experiment-tracking and registry setup
  • Hands-on Triton, TensorRT(-LLM) and vLLM deployment experience
  • A GitOps-based ML pipeline template
  • Honest metrics habits: every claim tied to a measurement
  • Preparation toward the NVIDIA NCP-AIO certification

How it runs

Every course follows the same model: 35% principles, 20% guided investigation, 45% engineering studio. You leave with working code, raw measurements and an evidence-based report — not a certificate of attendance. Read the methodology or see a full sample lesson.

Material is adapted to your kernel version, hardware and workload before a private delivery. For public cohorts, the environment is provided and configured.

Questions

Who is this course for?

Engineers owning the path from trained model to production — experiment tracking, registries, serving stacks and the CI/CD that keeps models improving safely. It sits at practitioner level within the GPU & AI Compute track.

What do I need to know already?

Specific prerequisites for this course: Python and basic ML workflow; Docker/Kubernetes basics; AIC-230 recommended. We confirm levels before the cohort starts and adapt if a group is stronger or weaker than expected.

Can this run privately for my team?

Yes. Any course runs on-site at your offices anywhere, or live online for a distributed team, with labs adapted to your hardware and codebase.

What is the difference between in-person and online?

In person is 3 full days with hardware on your desk, capped at 14. Online is 6 half-day sessions across about two weeks so you can keep working, capped at 20, with remote lab access.

Do you invoice companies?

Yes. Purchase orders are accepted and invoicing is available in USD, EUR, GBP, SAR and CAD.

Upcoming dates

DatesWhereSeatsEarly birdRegular
1 Nov – 3 Nov 20263 full days RiyadhIn person · KAFD Conference Centre 4 of 14 —SAR 7,880
8 Nov – 10 Nov 20263 full days Kuwait CityIn person · Al Hamra Tower 9 of 14 KWD 580until 9 OctKWD 650
15 Nov – 17 Nov 20263 full days MuscatIn person · Knowledge Oasis Muscat 4 of 14 OMR 730until 16 OctOMR 810
15 Nov – 22 Nov 20266 half-days Gulf bandLive online · 09:00–13:00 GMT+3 18 of 20 US$1,350until 16 OctUS$1,500
16 Nov – 18 Nov 20263 full days OttawaIn person · Kanata North Tech Park 9 of 14 CAD 2,570until 17 OctCAD 2,860
23 Nov – 25 Nov 20263 full days TorontoIn person · MaRS Discovery District 4 of 14 CAD 2,570until 24 OctCAD 2,860
23 Nov – 30 Nov 20266 half-days Europe bandLive online · 09:00–13:00 CET 7 of 20 US$1,350until 24 OctUS$1,500
30 Nov – 2 Dec 20263 full days LondonIn person · Shoreditch Works 9 of 14 GBP 1,480until 31 OctGBP 1,640
30 Nov – 7 Dec 20266 half-days Americas bandLive online · 13:00–17:00 ET 12 of 20 US$1,350until 31 OctUS$1,500
7 Dec – 9 Dec 20263 full days BerlinIn person · Factory Görlitzer Park 4 of 14 EUR 1,740until 7 NovEUR 1,930

Dates shown for the next few months. If nothing fits, tell us where and when — cohorts are added on demand, and private delivery can be scheduled any week.

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