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Complete learning course

DevOps / SRE + AI

Reliable operation of AI workloads

Serving, autoscaling, observability, cost control, security, and incident response for AI systems.

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10–16 weeks3 modules8 lessons5 assessments

Module 1

Shared AI core

Probabilistic failure modes and verifiable model behavior.

OutcomeSRE understands the operational risks and limits of AI systems.

  1. AI literacy and model limits

    Core

    Capabilities, hallucinations, context limits, privacy, and responsible use.

    Material →

  2. Prompt and context engineering

    Core

    Instructions, constraints, examples, and output verification.

    Prerequisites: AI literacy and model limits

    Material →

  3. Structured outputs and evaluation

    Core

    Response schemas, deterministic checks, and test cases.

    Prerequisites: Prompt and context engineering

    Material →Resource →

Module 2

AI runtime and serving

Inference, batching, queues, and scaling for production workloads.

OutcomeA controlled inference service with explicit capacity and health signals.

  1. LLM serving

    Corepractice + assessment

    Managed APIs, self-hosting, health checks, and runtime trade-offs.

    Material →Practice →Assessment →

  2. Autoscaling AI workloads

    Corepractice + assessment

    Capacity planning, warmup, saturation, and cost-aware scaling.

    Material →Practice →Assessment →

Module 3

Observability and incidents

Tracing, costs, alerts, rollback, and operational drills.

OutcomeAn AI platform with measurable SLOs and a tested recovery path.

  1. AI tracing and monitoring

    Corepractice + assessment

    Latency, quality, token usage, retries, errors, and service health.

    Material →Practice →Assessment →

  2. Project: AI operations dashboard

    Projectpractice + assessment

    SLOs, traces, token and cost metrics, alerts, and a controlled failure drill.

    Material →Practice →Assessment →

  3. AI incident response

    Corepractice + assessment

    Containment, fallback, rollback, communication, and postmortems.

    Material →Practice →Assessment →

Final practice and assessment

The course ends with a practical artifact and an acceptance rubric. The result is considered complete after the acceptance criteria are met, not merely after reading the materials.

LLM serving →Autoscaling AI workloads →AI tracing and monitoring →Project: AI operations dashboard →AI incident response →Open roadmap →