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Taught by Captain CloudbyteCloud & AI

Designing AI Workloads

Architect modern AI/ML systems with managed services, vector DBs, and inference at scale.

18 hours 19 lessons advanced
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Course overview

Move past prompt engineering and learn to design production AI systems: data pipelines, embeddings, retrieval, evaluation, and cost-aware inference.

What you'll learn

  • Architect a RAG system end to end
  • Choose between managed and self-hosted inference
  • Design evaluation and guardrails
  • Plan for cost, latency, and safety
Curriculum

5 modules · 19 lessons

  1. 01

    AI System Anatomy

    3 lessons
    • 1.1The modern AI stack 57 min
    • 1.2Where LLMs fit 57 min
    • 1.3Latency, cost, and quality tradeoffs 57 min
  2. 02

    Data & Embeddings

    4 lessons
    • 2.1Chunking strategies 57 min
    • 2.2Embedding models 57 min
    • 2.3Vector databases 57 min
    • 2.4Hybrid search 57 min
  3. 03

    Retrieval-Augmented Generation

    4 lessons
    • 3.1RAG architecture 57 min
    • 3.2Reranking 57 min
    • 3.3Caching 57 min
    • 3.4Streaming responses 57 min
  4. 04

    Evaluation & Safety

    4 lessons
    • 4.1Offline evals 57 min
    • 4.2Online evals 57 min
    • 4.3Guardrails 57 min
    • 4.4Red-teaming 57 min
  5. 05

    Production at Scale

    4 lessons
    • 5.1Inference platforms 57 min
    • 5.2Autoscaling GPUs 57 min
    • 5.3Cost optimization 57 min
    • 5.4Observability for LLMs 57 min
Ready when you are

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