Modules

14 topic modules, 110 lessons. A module lists one topic's lessons in teaching order; every lesson sits in exactly one module.

• System design

  The mechanisms every design is built from, taught as trade-offs with numbers, plus classic and AI-era design exercises and the real-world practice of design docs, postmortems and migrations.

  • 31 of 40 lessons published
  • 15 practice paths

• Software foundations

  Python from two angles, TypeScript on Node, React with Next.js, Postgres, and AI coding tools with production discipline.

  • 6 lessons
  • 6 practice paths

• APIs and enterprise integrations

  Connecting a system to the customer's systems of record: HTTP, JSON and OAuth 2.0, webhooks and Kafka events, and integrating with or migrating off legacy systems.

  • 3 lessons
  • 3 practice paths

• Identity and integration ops

  The hands-on work of connecting a product to a customer: SSO with OIDC and SAML against Okta, Entra ID or Keycloak, provisioning, machine-to-machine API auth, file feeds over SFTP, and testing and debugging all of it with Postman, Newman, curl and a SAML tracer.

  • 10 lessons
  • 10 practice paths

• Data platforms and pipelines

  The data layer under ML and AI work: Spark, lakehouse and stores, feature stores, cleaning and linking messy records, Snowflake and Databricks, dbt.

  • 6 lessons
  • 6 practice paths

• Workflow orchestration and durable execution

  Turning steps into repeatable, retryable pipelines: Airflow for ML and for data (with Dagster), the ML-native orchestrators, and Temporal.

  • 4 lessons
  • 4 practice paths

• The ML platform: clusters, GPUs and training

  What MLOps adds to software delivery; Kubernetes and EKS as the substrate; experiment tracking; GPUs as cluster resources; multi-node training; scheduling and tenancy of a shared cluster.

  • 8 lessons
  • 7 practice paths

• Model release engineering

  From a trained artifact to a version in production: registry and versioning, evaluation gates, CI/CD for ML, GitOps and Terraform, staged rollouts.

  • 5 lessons
  • 5 practice paths

• Model serving and inference

  Models as reliable inference services: serving architecture, SageMaker, open-weight LLM engines (vLLM, SGLang), TensorRT-LLM and edge, and GPUs inside a customer's own environment.

  • 5 lessons
  • 4 practice paths

• LLM application building blocks

  What every LLM system is assembled from: the model and its hosted APIs, prompt and context engineering, structured outputs and tool calling, retrieval over private data (two angles).

  • 6 lessons
  • 6 practice paths

• Agents and agentic workflows

  Agentic systems designed, connected, kept running, measured and constrained: agent architectures, MCP (both angles), durable orchestration, evaluation, guardrails, LLMOps.

  • 7 lessons
  • 6 practice paths

• Deploying into customer environments

  Running a system in infrastructure the customer owns: their cloud accounts, containers and CI/CD, serverless, restricted networks, and the cost and latency profile of LLM systems.

  • 4 lessons
  • 4 practice paths

• Operating in production

  After it ships: observability (platform and agent traces), model monitoring and drift, SLOs and on-call, debugging and postmortems, security and compliance, autoscaling and cost.

  • 8 lessons
  • 6 practice paths

• Delivery and platform practice

  How work is scoped, written down, piloted and turned into a platform others use: the forward-deployed delivery model, scoping and design docs, ADRs, workflow mapping, pilots, golden paths, reusable tooling.

  • 7 lessons
  • 5 practice paths