Is the AWS Bedrock Academy free?

Yes. The full course is free and includes 25 modules, 52 lessons, 16 labs, and quiz-based progression with no sign-up required.

How is Infinity AWS Bedrock Academy different from AWS Skill Builder?

Unlike AWS Skill Builder which often gates advanced hands-on labs behind paid subscriptions or AWS Builder ID logins, Infinity Tech Stack's AWS Bedrock Academy is 100% free, ungated, and browser-first with zero sign-up required. It includes 25 modules, 52 lessons, 16 hands-on labs, and 125+ quiz checks covering the Converse API, Bedrock Guardrails, AgentCore, and multi-model evaluations without requiring active AWS account credentials.

Who is this course for?

Developers, architects, and platform teams building AI systems on AWS Bedrock from MVP to production scale.

What does this Bedrock course cover?

The academy covers runtime inference, prompt engineering, guardrails, knowledge bases, agents, evaluation, observability, security, compliance, resilience, incident response, and production launch discipline.

Release logs should include:

Eval and incident correlation details — Operational traceability is critical for postmortems and governance.

Release Strategies and Rollbacks Tutorial | Bedrock with Containers

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1. Containerized Orchestration Services2. Release Strategies and Rollbacks

Release Strategies and Rollbacks

📚 Bedrock with Containers⏱ 10 min⭐ 110 XP

Safe Delivery for AI Backends

Use blue/green or canary releases with feature flags for prompt/model changes. Roll back quickly when quality or safety degrades. The twist versus classic services: an AI release can be code-identical and still behaviorally different - prompt text, model ID, guardrail config, and KB sync state are all release artifacts.

What Actually Constitutes a "Release"

release manifest v2026.07.16-r2
  code:        orchestration-svc:1.14.2
  prompt:      ticket-triage v3.2
  model:       amazon.nova-pro-v1:0 (pinned)
  guardrail:   gr-support vX7
  kb:          support-kb sync 2026-07-15
  eval:        golden 0.89 | safety 0.99 | p95 1780ms

If any element changes, the manifest version changes - and the eval gate reruns. This is what makes "what exactly did we ship?" answerable during an incident.

Rollout Mechanics

Strategy	Mechanism	Best for
Blue/green	Two task sets behind the LB; shift 100% atomically after bake	Model/prompt swaps where mixed versions confuse users
Canary	Weighted routing (1% → 25% → 100%) with metric gates per stage	Most changes; catches issues at minimal blast radius
Feature flags	Per-tenant/percentage toggles inside the service	Prompt variants, new tools, A/B experiments

Rollback Reality

Rollback must revert the whole manifest - reverting code while keeping the new prompt is a half-rollback that debugs like a haunted house.
Keep the previous manifest warm and deployable; config-driven model/prompt IDs make revert a one-line change.
Keep release logs tied to eval scores and incident timelines - when an incident starts, the first question is "what changed and what were its scores?".

Bake time matters: LLM regressions often surface on traffic diversity, not volume. Hold canary stages long enough to see peak-hour and edge-case traffic before promoting.

🧪 Knowledge Check

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A safe release for AI backends includes:

No rollback path

Canary/blue-green with score-gated promotion

Immediate global rollout always

No traceability