Week 8: Specialized Hardware & AI-Assisted Operations (Days 50-56)
What We Build
You will design and operate Nexus AI Operations Platform — a single control plane that treats specialized accelerators, edge inference nodes, ML-driven infrastructure decisions, secure delivery tooling, responsible-AI governance, and autonomous remediation as one coherent system rather than a collection of disconnected tools.
The platform is built from scratch as one product: every module shares the same API process, the same console, and the same operational vocabulary. There are no separate mini-projects stitched together at the UI — compute, edge, intelligence, governance, and automation are first-class domains inside one repository.
Agenda
Compute orchestration — GPU multi-instance sharing, placement policies, and spend visibility
Accelerator scheduling — TPU pod queues with preemptible cost optimization
Edge fleet management — device registration, model rollout, heartbeat health, cloud sync
Infrastructure intelligence — forecasting, anomaly detection, predictive scaling, incident response
Delivery intelligence — code security analysis, log anomaly detection, alert correlation, documentation generation
Model governance — bias analysis, fairness monitoring, explainability, approval workflows
Autonomous operations — DAG workflows, self-healing, controlled chaos, AI-assisted scheduling
Unified console — real-time dashboard with cross-domain Run Demo simulation
Platform objective: Deliver the integration pattern production ML platform teams use before opening a shared GPU pool to multiple product lines — one API boundary, explicit promotion gates, observable state, and automation that closes the loop.
Why a Unified Control Plane Matters
In most organizations, GPU scheduling lives in one team’s scripts, edge rollout in another’s Ansible playbooks, bias review in a spreadsheet, and incident correlation in a third-party SaaS. Each layer works in isolation until something breaks at a boundary — a model promoted without review, inference scaled without reading logs, or a TPU job submitted onto full-GPU capacity because nobody exposed MIG slices.
Nexus models the opposite architecture: one FastAPI application registers seven domain routers; one React console polls them; one demo orchestrator proves they cooperate. That shape mirrors how mature internal platforms (not public cloud consoles) are actually operated:
You are not learning seven unrelated APIs — you are learning how platform engineers wire them together.
Platform Placement in the Overall System
Modern AI infrastructure at scale (recommendation training at Meta, TPU fleets at Google, factory-floor inference at industrial operators) converges on three planes that must cooperate:
Nexus is the coordination layer between product engineers and raw infrastructure. It does not replace Kubernetes, cloud consoles, or observability backends. It exposes a stable internal API so an inference team can submit a job without knowing whether it lands on a MIG slice, a preemptible TPU pod, or an edge CPU node — while SRE and platform teams retain policy control behind that abstraction.
Core Concepts by Engineering Discipline
