AWS & DevOps services

AWS AI Services

Plan and implement practical AI workloads on AWS with use-case qualification, data and access boundaries, evaluation, delivery, observability, and ownership.

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Scope

What this service can cover

The exact implementation follows the environment and the signed scope. These are the technical workstreams most often composed for AWS AI Services.

01

Use-case and readiness

Define the user, decision, data, acceptable failure, human review, evaluation criteria, cost boundary, and production owner.

02

AWS service architecture

Select Bedrock, SageMaker, managed AI services, event workflows, storage, compute, and integrations based on the use case.

03

Security and evaluation

Implement IAM, network, encryption, data boundaries, guardrails, test sets, quality checks, and failure handling.

04

Delivery and operations

Build infrastructure as code, pipelines, observability, cost controls, rollback paths, and an owned improvement backlog.

Work products

What the client receives

  • Use-case and readiness decision record
  • AWS AI architecture and threat boundaries
  • Implemented infrastructure, workflow, and evaluation harness
  • Runbooks, cost visibility, and handoff
Delivery approach

From current state to client-owned handoff

01

Qualify

Define the user, decision, source data, expected output, failure cost, and whether AI or deterministic automation fits.

02

Bound

Set data, identity, model, tool, review, security, cost, and observability boundaries before implementation.

03

Build and evaluate

Implement the workflow with representative tests, explicit quality criteria, failure paths, and controlled deployment.

04

Operate and hand off

Transfer code, prompts, evaluation assets, runbooks, budgets, monitoring, rollback, and accountable ownership.

Ways to engage

Match the commercial model to the work

Defined project

Professional Services & Consulting

Fixed fee or time and materials for a scoped outcome, implementation, acceptance, and handoff.

 Recurring capacity

Managed DevOps / Support & Advisory

Monthly business-hours engineering capacity against a shared Jira backlog.

 Clarify first

Assessment Programs

Use a focused diagnostic when the current state or right next step is not yet clear.
Related paths
Build Generative AI on AWSTake a qualified generative AI use case from data and control boundaries through Bedrock architecture, evaluation, production delivery, and ownership. Generative AI on AWSQualify and build generative AI applications on AWS with retrieval, agents, evaluation, human review, data controls, and observable failure handling. Amazon Bedrock ConsultingImplement Amazon Bedrock foundations and applications with model access, IAM, private networking, knowledge bases, agents, guardrails, evaluation, and cost controls. MLOps ConsultingBuild reproducible machine-learning delivery with data and model versioning, training and deployment pipelines, evaluation, monitoring, rollback, and ownership.
FAQ

Scoping AWS AI Services

Does AI work begin with model selection?

Not by default. It begins with the user, data, decision, failure cost, evaluation method, and whether probabilistic output is appropriate.

How is quality evaluated?

The scope defines representative test inputs, quality criteria, failure cases, human review, release thresholds, and production observations.

Who controls data and model access?

The client retains its accounts, data decisions, credentials, and provider agreements; the implementation documents permissions and operating ownership.

Bring the environment and the decision you are facing.

Use the free hour to work through the current state and identify a useful next step before you commit to a project.

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