Production MLOps & AI Platform Engineering

Understand the lifecycle and constraints before choosing platform machinery.

• Lifecycle stages, architecture, reproducibility, and environments
• System constraints, ownership, interfaces, and failure domains
• Design docs, tradeoffs, and a practical platform vocabulary

Deliverable: a platform design document with boundaries, ownership, and operating assumptions.

Make training inputs and outputs inspectable, repeatable, and attributable.

• Validation, feature pipelines, orchestration, and lineage
• Artifact and version management across data and models
• Reproducible runs, checks, and useful metadata

Deliverable: a repeatable training pipeline with validation checks and lineage notes.

Turn a model artifact into a release path that another engineer can operate.

• Containers, batch and online serving, and service APIs
• CI/CD, infrastructure configuration, and environment promotion
• Release checks, rollback points, and documentation

Deliverable: an automated model release with deployment notes and a rollback procedure.

Give operators enough signal to see when the system or its data has changed.

• Service health, drift, data quality, and model quality
• Alert thresholds, rollback, and incident playbooks
• SLO thinking and the difference between noise and action

Deliverable: a monitoring dashboard and runbook for a defined production service.

Apply platform judgment to newer model workloads and their cost profile.

• Prompt and model registries, evaluation gates, and caching
• Inference optimization, throughput, latency, and cost control
• Capacity assumptions, privacy boundaries, and release evidence

Deliverable: a governed LLM release pipeline with evaluation and cost gates.

Design the golden path that lets a team move faster without erasing its boundaries.

• Self-service interfaces, security boundaries, and documentation
• SLOs, ownership, incident response, and platform adoption
• Reference implementation walkthrough and case-study writing

Capstone: a production AI platform reference implementation with golden path, SLOs, security notes, and operator runbook.