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MLOps

Building an AWS MLOps Platform: From Local Training to Managed ML Workflows

A practical look at evolving a reproducible local machine-learning lifecycle into an AWS platform, with SageMaker training, S3 artifacts, IAM boundaries, evaluation gates, and a phased production roadmap.

Production MLOps is more than model training

A model-training script is only one part of a production machine-learning system. Teams also need a repeatable way to prepare data, evaluate model quality, track artifacts, control access, and eventually promote approved models into a serving environment. The platform challenge is connecting those steps without introducing operational complexity before it is needed.

The AWS Production MLOps Platform project explores that progression incrementally. Its model is intentionally simple; the focus is the infrastructure and engineering practices around the model lifecycle.

Start with a reproducible local lifecycle

The initial phase uses a synthetic lending dataset and a scikit-learn pipeline. The dataset is split into training and test sets with a fixed random seed, then processed with standard scaling and logistic regression. Keeping the example small makes it easier to focus on lifecycle controls rather than domain-specific modeling.

Training and evaluation are separate steps. Evaluation checks the model against minimum quality criteria and exits unsuccessfully when those criteria are not met. That quality gate gives later automation a clear rule: a model that fails evaluation should not move forward for promotion or deployment.

Model files are accompanied by metadata and evaluation results. Together, these artifacts make it possible to inspect not just which model was produced, but also the configuration and outcome associated with it.

Move execution to managed training

The next phase moves the training workload to Amazon SageMaker. Training data is stored in Amazon S3, and SageMaker runs the training job using the project’s training code and a dedicated execution role. The trained model is written to the SageMaker model-artifact location and packaged for durable storage in S3.

This separation establishes a useful platform boundary: a developer can initiate a managed job, while the service assumes an explicitly configured role to access the data and write its output. The role should remain limited to the resources and actions the workload needs; it should not be replaced with broad account credentials.

Managed training also makes the execution environment more explicit than a developer’s laptop. Job configuration, input locations, role selection, and generated artifacts become part of the workflow to review and improve as the platform evolves.

Use quality gates to control promotion

Training a model does not mean it is ready to serve. The project’s lifecycle places evaluation after training and before promotion. In a mature workflow, that gate can be extended with agreed metrics, thresholds, and approval policies, so model movement is based on evidence instead of simply on whether a training job completed.

  1. Run training against a defined dataset and configuration.
  2. Record the resulting model artifact and its lineage.
  3. Evaluate the candidate model against explicit quality criteria.
  4. Stop or flag candidates that do not meet the criteria.
  5. Only introduce registration and deployment after their controls are implemented.

That final distinction matters: the repository’s roadmap includes experiment tracking, model registry, inference, CI/CD, infrastructure as code, Kubernetes serving, and observability as future phases. They are planned extensions, not capabilities to assume are already available in the current implementation.

Build the platform in validated stages

Incremental delivery keeps the design testable. A local lifecycle can first prove that training, artifact generation, and evaluation behave as expected. Managed training can then be added and validated independently before introducing registries, endpoints, or orchestration.

This sequencing also helps manage cost. The project avoids introducing always-running infrastructure such as an EKS cluster during early phases, when the core objective is to validate the lifecycle and managed training path. Cloud resources should be added when the architecture requires them, with their costs and access boundaries understood.

What to examine in an MLOps implementation

  • Reproducibility: Are data inputs, training settings, and model outputs identifiable?
  • Quality controls: Can a failed evaluation prevent a candidate from progressing?
  • Access boundaries: Does each workload use a dedicated, appropriately scoped IAM role?
  • Artifact handling: Are model outputs stored separately from source code and associated with useful metadata?
  • Operational scope: Are planned platform features clearly distinguished from implemented ones?
  • Cost awareness: Is infrastructure introduced only when needed for the current phase?

Explore the project

This article summarizes the design direction and current implementation phases. For the source code, architecture details, setup instructions, and the up-to-date roadmap, visit the AWS MLOps Platform repository on GitHub.