7.2 RAG and Feature Infrastructure¶

Role at Stage 7: Model Infrastructure¶

Operate embeddings, vector indexes, feature artifacts, and refresh jobs as production assets. This part is one capability inside the stage. It should leave behind an artifact, measurements, and a short explanation of failure modes.

Explanation¶

This part has 4 sub-parts because the topic needs that many learning units to feel natural. Some stages have more parts and some have fewer; the structure follows the topic, not a fixed template.

Part Diagram¶

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flowchart LR
  P["<b>7.2</b><br/>RAG and Feature<br/>Infrastructure"]
  P --> S1["<b>7.2.1</b><br/>Embedding Jobs"]
  P --> S2["<b>7.2.2</b><br/>Vector Store<br/>Operations"]
  P --> S3["<b>7.2.3</b><br/>Feature Stores and<br/>Artifacts"]
  P --> S4["<b>7.2.4</b><br/>Index Refresh and<br/>Rollback"]
  P --> E["<b>Exam</b><br/>Part practice"]

Sub-Parts¶

Sub-part folder	What it explains
7.2.1 Embedding Jobs	Embedding Jobs is the working skill inside RAG and Feature Infrastructure that helps you build the stage artifact, A deployed model-backed service with data or retrieval pipeline, registry metadata, eval checks, structured logs, and dashboards, while collecting enough evidence to trust the result.
7.2.2 Vector Store Operations	Vector Store Operations is the working skill inside RAG and Feature Infrastructure that helps you build the stage artifact, A deployed model-backed service with data or retrieval pipeline, registry metadata, eval checks, structured logs, and dashboards, while collecting enough evidence to trust the result.
7.2.3 Feature Stores and Artifacts	Feature Stores and Artifacts is the working skill inside RAG and Feature Infrastructure that helps you build the stage artifact, A deployed model-backed service with data or retrieval pipeline, registry metadata, eval checks, structured logs, and dashboards, while collecting enough evidence to trust the result.
7.2.4 Index Refresh and Rollback	Index Refresh and Rollback is the working skill inside RAG and Feature Infrastructure that helps you build the stage artifact, A deployed model-backed service with data or retrieval pipeline, registry metadata, eval checks, structured logs, and dashboards, while collecting enough evidence to trust the result.

What a Person Who Masters This Part Can Do¶

Explain how RAG and Feature Infrastructure supports a deployed model-backed service with data or retrieval pipeline, registry metadata, eval checks, structured logs, and dashboards..
Build and inspect this artifact: Build a rebuildable vector index pipeline.
Measure progress with: Track chunks, embedding model, metadata, index version, and rollback.
Debug at least one failure mode before moving to the next part.

Build and Measure¶

Build: Build a rebuildable vector index pipeline.

Measure: Track chunks, embedding model, metadata, index version, and rollback.

Tests¶

Take one 30-question exam after studying this part. It opens in a new browser tab so the study page stays available.

Open Part Exam

Back to Stage¶

Return to Stage 7: Model Infrastructure.