A 16-week graduate course. AI as the tool you wield — and the workload you govern.

ADMINISTER THE
AI-ERA ENTERPRISE.

COACH

DR. MICHAEL LITMAN, Ph.D.

Master Computer Scientist

Every administrator, cloud engineer, DevOps engineer, security analyst, and SRE now works beside artificial intelligence — and is increasingly asked to run the infrastructure AI itself depends on. This course refuses the bolted-on "AI week." AI runs through all sixteen: how it changes the administrator's daily work, and how to administer the GPUs, models, and pipelines that AI workloads demand. You will collaborate with AI, manage AI infrastructure, defend against AI-powered threats, and govern AI systems responsibly.

START WEEK 1 SEE THE SYLLABUS

16

Chapters

14

Projects + Labs

AI

Woven through all 16

8+8

Foundations → Operations

PHASE 1 · FOUNDATIONS IN THE AI ERA · WEEKS 1–8

Build the foundation.

The role, the operating system, identity and access, storage, networks, virtualization, containers, and automation — the classical building blocks of enterprise administration, each taught with AI woven through as a tool you direct and a workload you must run. You cannot govern AI infrastructure you have never stood up by hand.

PHASE 2 · OPERATE, DEFEND, GOVERN · WEEKS 9–16

Run it at scale.

Observability and AIOps, security operations against AI-powered adversaries, backup and continuity for AI assets, the cloud and its costs, DevOps and SRE, lifecycle and governance, autonomous self-healing operations — and a capstone in which you architect the AI-native enterprise of 2030, end to end.

THE MASTER'S-LEVEL OUTCOME

Evaluate, deploy, govern, and maintain enterprise infrastructure in environments where artificial-intelligence systems act as both operational tools and managed workloads.

— SYLLABUS

Phase 1 · Foundations in the AI Era

Weeks 1–8. The building blocks of administration, each connected to the AI reality you actually work in.

Ch 1 — The Administrator in the Age of AI

The role of the sysadmin, enterprise infrastructure, and IT operations; the shift from manual administration to AIOps, AI copilots, and AI-assisted documentation — and its hallucinations

P1 From Runbook to Copilot

Ch 2 — The Machine Underneath: Operating System Architecture

Processes, memory, storage, and services; reading process tables and kernel/event logs with and without AI; running local models, CPU vs GPU, and quantization

P2 Diagnose the Failure Twice

Ch 3 — Identity and Access in a World of Synthetic Faces

Identity and access management, authentication and authorization; AI threats (deepfakes, voice cloning, synthetic identities); AI-based anomaly detection of logins and privilege escalation

P3 Catch the Impostor

Ch 4 — Storage Administration and the Weight of Data

Storage administration; storage for AI systems (model weights, vector databases, embeddings); data governance, retention, and sensitive training data

P4 Size the AI Estate

Ch 5 — Network Services and the Watch on the Wire

Network services administration; AI-enhanced monitoring and behavioral baselines; the network requirements of GPU clusters, distributed inference, and AI APIs

P5 Read the Traffic

Ch 6 — Virtualization and the Shape of the Machine

Virtualization technologies; GPU passthrough and vGPU/MIG; scheduling AI workloads against traditional workloads

P6 Carve the GPU

Ch 7 — Containers and the Sending Out

Containers and modern deployment; containerized AI applications and microservices; model serving with Ollama, vLLM, and Open WebUI; deploying a local LLM in a container

P7 Ship a Model in a Box

Ch 8 — Infrastructure as Code, and the Limits of the Servant

Infrastructure automation; AI-generated infrastructure-as-code, prompt engineering for automation, and validating it; hallucinated configurations and security risks; plus the cumulative midterm

P8 Generate, Verify, Trust (MIDTERM)

Phase 2 · Operate, Defend, Govern

Weeks 9–16. Running, defending, and governing the AI-era enterprise at scale.

Ch 9 — Monitoring, Observability, and Keeping Watch

Monitoring and observability; AIOps platforms for predictive monitoring, root-cause analysis, and event correlation; AI-based log analysis and natural-language questions about logs

P9 Ask Your Logs

Ch 10 — Security Operations: The Adversary Who Disguises Himself

Security operations; AI as attacker (phishing campaigns, AI-generated malware, automated reconnaissance) and AI as defender (threat detection, behavioral analysis, automated response); the OWASP Top 10 for LLM applications

P10 Attacker and Defender

Ch 11 — Backup, Recovery, and the Ark You Build Before the Flood

Backup, recovery, and business continuity; protecting AI assets (model backups, vector-database backups, prompt libraries, fine-tuning datasets); designing disaster-recovery plans for AI-enabled organizations

P11 DR for the AI Estate

Ch 12 — Cloud Systems Administration and Counting the Cost

Cloud systems administration; cloud AI services (OpenAI, Azure AI Foundry, AWS Bedrock, Google Vertex AI); AI cost management — token, GPU, and fine-tuning costs; deploying an AI workload in the cloud and analyzing its cost

P12 Deploy in the Cloud, Read the Bill

Ch 13 — DevOps, SRE, and Work That Lasts

DevOps and site reliability engineering; AI-augmented DevOps (AI-assisted CI/CD, AI-generated tests, AI code review); the reliability of AI systems — model drift, prompt failures, and hallucination monitoring

P13 A Pipeline for an AI Application

Ch 14 — Maintenance and the Lifecycle of Things

Enterprise maintenance and lifecycle management; managing AI infrastructure (model updates, versioning, governance); the AI asset lifecycle — retirement, dataset management, and compliance

(no new project — lifecycle drills + capstone prep)

Ch 15 — AIOps and Autonomous Operations: How Much May We Entrust?

AIOps platforms (Splunk AI, Dynatrace, Datadog AI, Microsoft Copilot for operations); autonomous remediation — self-healing infrastructure, AI-generated fixes, automated incident response; human oversight, trust, accountability, and governance

(no new project — autonomy drills + final prep)

Ch 16 — The AI-Native Enterprise: Capstone

Capstone — design an organization operating in 2030 with both traditional components (identity, virtualization, monitoring, security, backup) and AI components (local LLM infrastructure, AI governance, AI-assisted monitoring and automation, AI security controls, AI disaster recovery)

P14 Architect the 2030 Organization (FINAL)