AI & Machine Learning💻 Technical CourseLearnAspire Certified

Modern AI Platform Engineering: The Architect's Blueprint for Production AI Systems at Scale

“From Prototype to Platform: Architect AI Infrastructure Your CTO Will Sign Off On”

Design and deploy a complete production AI platform — retrieval, inference, LLMOps, observability, and governance — and graduate with a complete AI Architecture Document.

Advanced15h9 modules83 slides27 exercises36 quiz Qs
🔥 Launch Price — 63% off. Limited time.
₹2,999₹7,999

One-time · Lifetime access · Certificate included

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7-day money-back guarantee
  • ✓9 modules of content
  • ✓83 concept slides
  • ✓27 practical exercises
  • ✓36 quiz questions
  • ✓Capstone project
  • ✓LearnAspire certificate

Learning Outcomes

What you'll learn

→You will be able to design a complete AI platform stack — retrieval, inference, orchestration, and observability — and document it as a C4-level architecture diagram with justified trade-offs
→You will be able to select and configure inference infrastructure (vLLM, LiteLLM, or managed APIs) based on cost, latency, and compliance constraints specific to your company
→You will be able to build a production RAG pipeline with a vector database (Qdrant or pgvector), chunking strategy, and retrieval evaluation using RAGAS or similar
→You will be able to implement an LLMOps pipeline including prompt versioning, CI/CD for prompt changes, regression testing, and a/b evaluation with Langfuse or MLflow
→You will be able to instrument an AI platform with OpenTelemetry traces, cost-per-token dashboards, and drift detection alerts — and write the governance policy documents required for enterprise AI compliance

The day after you finish

The day after completing this course, you will open your company's AI project and produce a draft AI Platform Architecture Document — covering model selection rationale, retrieval design, inference routing, observability stack, and governance controls — ready to present to your CTO or engineering leadership.

Who this is for

  • Primary: Platform engineers and solution architects who build or evaluate production AI systems
  • Secondary: Tech leads and engineering managers deciding AI infrastructure strategy
  • Tertiary: Senior software engineers transitioning into AI platform roles

Prerequisites

  • Hands-on experience with LLM APIs (OpenAI, Anthropic, or similar)
  • Comfortable reading Kubernetes manifests and Python code
  • Basic understanding of cloud infrastructure (AWS, GCP, or Azure)
  • Experience building or operating distributed backend systems

Curriculum

9 modules · full breakdown

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Capstone Project

AI Platform Architecture Document for Veridian Pay

Design the complete AI platform architecture for Veridian Pay's three AI initiatives (fraud explanation, contract analysis, developer code assist). The architecture must cover: model selection with vendor comparison, RAG pipeline design with chunking and retrieval strategy, inference routing with cost and latency projections, LLMOps CI/CD pipeline, observability stack with OpenTelemetry, and a governance policy covering data handling, content filtering, and audit logging.

What you'll deliver

A complete AI Platform Architecture Document (12-15 pages) with C4 context and container diagrams, ADRs for each major decision, a cost model for 3M tokens/day, and a governance checklist aligned to UK financial services regulations.

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