LOCKEDIN / FORWARD-DEPLOYED ENGINEERING 101
Understand it.
Then build it.
You can be an experienced engineer and still be new to agents. Start with what each thing is and how it helps a person do useful work. Explore 48 original introductions across software, agents, cloud, security and delivery. Start here without a model key; each topic connects to deeper instruction and practice.
- 101UnderstandPlain language, visual walkthrough, vocabulary
- 102Build with guidanceOriginal lessons, examples and knowledge checks
- 103Practise and explainRunnable labs, failure cases and handover
- 01Scope the workload
- 02Connect the service
- 03Prove the result
A managed foundation-model service
What is Amazon Bedrock?
Amazon Bedrock is an AWS service for using foundation models and building generative-AI applications. It provides managed model access; your application still needs identity, authorized data, bounded workflows and evaluation. It is not a model family or a replacement for the rest of your application.
Many customers already have AWS identity and network controls that the AI application must fit.
CONNECT IT TO WHAT YOU KNOW
Start with something familiar.
It is a managed inference dependency inside an AWS architecture, just as a managed database is one dependency rather than the whole product.
Verify supported models, regions, interfaces and quotas for the chosen account. A model accessible through one provider route is not automatically identical to another route operationally.
Words worth knowing
- Foundation model
- A broadly trained model that can be applied to many tasks.
- IAM
- Identity and Access Management: policies controlling AWS access.
- Inference endpoint
- An interface used to request model output.
ONE EXAMPLE / FICTIONAL CASE
Watch the work take shape.
A worker uses a permitted Bedrock model to draft a response from approved procedure excerpts, then stores the draft for review.
- 01Scope the workload
- 02Connect the service
- 03Prove the result
Scope the workload
Choose the identity, region and model access needed for one bounded task.
A least-privilege planFROM RECOGNITION TO REASONING
Write an AWS deployment decision record.
- 01
Name the application worker, source store, model service and review store.
- 02
Identify permissions for each connection.
- 03
Specify what happens when model quota is exhausted.
A bounded architecture with a quota-failure path.
Compare your reasoning when you are ready
Quota exhaustion should produce a controlled wait, rejection or approved fallback. It should not silently send customer data to an unapproved provider.
Keep notes in your own working copy. Moving through these cards does not record completion.
TURN THE IDEA INTO USEFUL WORK
You can now go deeper.
Verify supported models, regions, interfaces and quotas for the chosen account. A model accessible through one provider route is not automatically identical to another route operationally.
Academy lessons use your existing training access. Free guides stay open.
Learn with the original creators
Use this companion course for the Claude-on-AWS path; confirm current availability in AWS documentation.