7.4 Serving and Deployment¶

Role at Stage 7: Model Infrastructure¶

Expose model behavior through reliable APIs, containers, streaming, batching, and deployment environments. 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>7.4</b><br/>Serving and Deployment"]
  P --> S1["<b>7.4.1</b><br/>Inference APIs and<br/>Streaming"]
  P --> S2["<b>7.4.2</b><br/>Containers and Runtime<br/>Images"]
  P --> S3["<b>7.4.3</b><br/>Cloud Deployment"]
  P --> S4["<b>7.4.4</b><br/>Queues Batching and<br/>Autoscaling"]
  P --> E["<b>Exam</b><br/>Part practice"]

Sub-Parts¶

Sub-part folder	What it explains
7.4.1 Inference APIs and Streaming	Inference APIs and Streaming is the working skill inside Serving and Deployment 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.
7.4.2 Containers and Runtime Images	Containers and Runtime Images is the working skill inside Serving and Deployment 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.
7.4.3 Cloud Deployment	Cloud Deployment is the working skill inside Serving and Deployment 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.
7.4.4 Queues Batching and Autoscaling	Queues Batching and Autoscaling is the working skill inside Serving and Deployment 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 Serving and Deployment supports a deployed model-backed service with data or retrieval pipeline, registry metadata, eval checks, structured logs, and dashboards..
Build and inspect this artifact: Deploy a model-backed endpoint.
Measure progress with: Track cold start, p95, throughput, errors, and reproducibility.
Debug at least one failure mode before moving to the next part.

Build and Measure¶

Build: Deploy a model-backed endpoint.

Measure: Track cold start, p95, throughput, errors, and reproducibility.

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.