NeuralArmada | MLOps on SageMaker AI

A model in a notebook is a science project. A model that retrains on schedule, registers itself with its metrics, deploys through an approval gate, and alerts you when its inputs drift is a product. MLOps is the gap between the two, and SageMaker AI has grown a complete toolset for closing it. We design and implement that toolset for teams who would rather not learn it the hard way.

Pipelines and the Model Registry

The backbone of every SageMaker MLOps setup we build is a SageMaker Pipeline (preprocess, train, evaluate, conditionally register) feeding the Model Registry (versioned model packages with metrics, lineage, and approval status). From there a registry approval event triggers deployment through EventBridge, so promoting a model to production is a reviewed click rather than a manual script.

We write these pipelines with the current SageMaker Python SDK and the @step decorator where it simplifies things, and we check the definitions into your repository alongside unit tests. See our tutorial, A Minimal MLOps Loop: SageMaker Pipelines + Model Registry, for the shape of it.

Feature Store

When several models share the same customer, product, or session features, copy-pasting feature code is how training-serving skew starts. We stand up SageMaker Feature Store with online and offline stores, point-in-time-correct joins for training datasets, and streaming ingestion where a feature must be fresh at inference time.

Model Monitor and drift

We configure SageMaker Model Monitor for data quality, model quality (when ground truth arrives later), bias, and feature attribution drift, with baselines captured from the training set and alerts wired to the channels your on-call team actually reads. Where Model Monitor's schedule-based approach does not fit, we build custom monitoring on endpoint data capture.

Multi-account MLOps landing zones

Production ML needs the same account separation as any other workload: a data account, dev / test / prod ML accounts, shared services for the registry and artifacts, and cross-account deployment roles. We build these landing zones as AWS CDK or Terraform code, with SageMaker Studio domains, VPC-only networking, KMS encryption, and IAM scoped to the minimum that lets the pipeline run.

CI/CD for models and infrastructure

Model code, pipeline definitions, and infrastructure all live in git and ship through CI. We integrate with GitHub Actions, GitLab CI, or AWS CodePipeline, add pipeline dry-runs and container builds to the pull-request checks, and use SageMaker Projects templates where they help and plain CDK where they do not.

Cost governance

Training jobs and endpoints are the easiest AWS spend to lose track of. We implement tagging standards, per-team budgets and alerts, idle-endpoint detection, spot training with checkpointing, and right-sizing reviews, and we report the savings against the baseline in the first month.

MLOps maturity assessment

Not sure where to start? We offer a fixed-scope MLOps maturity assessment: a one-week review of your current training, deployment, monitoring, and governance practice, scored against a maturity model, with a prioritized roadmap and effort estimates. It works equally well for teams with one model in production and teams with fifty.

Why NeuralArmada

Senior, US-based consultants who build this on SageMaker every week.
Infrastructure as code, reviewed in pull requests, handed over with documentation.
Engagements sized to your team: a one-week assessment, a pipeline build, or a standing MLOps engineer embedded with your group.