AWS & DevOps services

MLOps Consulting

Build reproducible machine-learning delivery with data and model versioning, training and deployment pipelines, evaluation, monitoring, rollback, 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 MLOps Consulting.

01

Lifecycle and reproducibility

Define source, data, feature, environment, experiment, artifact, approval, and lineage boundaries.

02

Training and registry pipelines

Automate preparation, training, evaluation, registration, security checks, and promotion using SageMaker or approved tools.

03

Deployment patterns

Implement batch, real-time, asynchronous, or edge deployment with environment controls, canaries, and rollback.

04

Model operations

Monitor data and model behavior, quality, latency, drift signals, cost, incidents, retraining, and owner decisions.

Work products

What the client receives

  • MLOps architecture and lifecycle definition
  • Training, evaluation, and deployment pipelines
  • Registry, monitoring, and rollback controls
  • Documentation and operating 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
AWS AI ServicesPlan and implement practical AI workloads on AWS with use-case qualification, data and access boundaries, evaluation, delivery, observability, and ownership. AI Platform InfrastructureBuild AWS accounts, networks, identity, data, compute, infrastructure as code, delivery, observability, and cost controls for AI workloads. GitHub Actions and CI/CDDesign and implement secure GitHub Actions workflows for build, test, infrastructure, promotion, deployment, and release evidence.
FAQ

Scoping MLOps Consulting

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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