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MLOps Projects · Production implementations

MLOps Projects: End-to-End, AWS, Azure and GCP Examples

Inspect verified MLOps project implementations — from data versioning through deployment, monitoring, and rollback — with explicit evidence states and cloud-platform mappings.

Written byAshutosh· AI InstructorVerified byVivek· AIOps and Generative AI InstructorUpdatedOfficially documented

What makes an MLOps project production-ready?

A production MLOps project connects versioned code and data to reproducible training, evaluation, model registration, controlled deployment, telemetry, and rollback. It is not a notebook with an API. It carries identity through every artifact, proves deployment with prediction tests, monitors for drift, and can recover from failure. This hub links to verified implementations on AWS, Azure, and GCP with explicit evidence states.

Difficulty
intermediate
Environment
Multi-platform
Last verified
2026-08-19

What makes an MLOps project production-ready?

A training notebook plus an API endpoint is not an MLOps project. A production MLOps project carries identity through every stage — from source commit and dataset version through training run, evaluation report, registered model, approved release, deployed revision, telemetry baseline, and rollback target.

The distinction matters because production systems fail in ways notebooks do not. Data drifts, models degrade, dependencies break, and traffic patterns change. Without version identity, observability, and rollback, you cannot diagnose or recover from these failures. You are running hope-driven operations.

  • Version identity: every artifact can be traced to its source commit, dataset version, and training run
  • Evaluation: model quality is measured against a golden dataset before and after deployment
  • Deployment revision: the running model has a known version that can be compared, rolled back, or promoted
  • Observability: latency, prediction distribution, and data drift are monitored with alerts
  • Failure recovery: rollback targets and runbooks exist before the first incident

The evidence required at each maturity level

  1. 1

    Notebook

    Code runs in a Jupyter notebook with manual steps and no version control.

    Evidence required: Reproducible execution on a clean environment

  2. 2

    Reproducible

    Training is scripted, dependencies are pinned, and data is versioned.

    Evidence required: Two runs produce identical results from the same commit and data

  3. 3

    Automated

    A pipeline orchestrates training, evaluation, and registration without manual steps.

    Evidence required: Pipeline DAG execution log with stage-level success

  4. 4

    Deployed

    The model is served behind an API with health checks and a known revision.

    Evidence required: Prediction test against the live endpoint

  5. 5

    Monitored

    Latency, drift, and prediction quality are tracked with alerting.

    Evidence required: Monitoring dashboard with alert rules and a baseline

  6. 6

    Operated

    The system has rollback, retraining triggers, and incident runbooks.

    Evidence required: Rollback test or failure recovery demonstration

End-to-end MLOps project

  • End-to-End MLOps Project: Build, Deploy, Monitor and Retrain

    One connected lifecycle from data versioning through rollback with the real SCAI stack.

    advancedDocker/KubernetesOfficially documented
  • AWS MLOps Project with Amazon SageMaker

    SageMaker lifecycle from training through serverless endpoint deployment with verified SCAI evidence.

    advancedAWSPartially tested
  • Azure MLOps Project with Azure Machine Learning

    Azure ML lifecycle blueprint — pending execution evidence.

    advancedAzureOfficially documented(in review)
  • GCP MLOps Project with Vertex AI

    Vertex AI lifecycle blueprint — pending execution evidence.

    advancedGCPOfficially documented(in review)
  • MLOps Project Ideas: Beginner to Production

    Choose a project with realistic scope, acceptance criteria, and evidence requirements.

    beginnerMulti-platformOfficially documented

Choose a project by outcome and environment

Each row links to a published page with full architecture, evidence, and limitations.

OutcomeDifficultyEnvironmentEvidence
Build a complete lifecycle from data to rollbackadvancedDocker/KubernetesOfficially documented
Deploy a model on AWS SageMakeradvancedAWSPartially tested
Implement MLOps on Azure Machine LearningadvancedAzureOfficially documented
Implement MLOps on Google Cloud Vertex AIadvancedGCPOfficially documented
Find a project idea with realistic scopebeginnerMulti-platformOfficially documented

Cloud MLOps implementations

  • AWS MLOps with Amazon SageMaker
    Partially tested
  • Azure MLOps with Azure Machine Learning
    Officially documentedin review
  • GCP MLOps with Vertex AI
    Officially documentedin review

How School of Core AI labels project evidence

Every project page carries an explicit evidence state. This separates what SCAI has actually executed from what is documented from official sources or planned for future validation.

Tested by SCAI means the team ran the code, observed the output, and recorded the artifact. Partially tested means some stages were executed and others are documented. Officially documented means the claim comes from current vendor documentation, not SCAI execution. Planned validation means the implementation is designed but not yet executed.

How this hub connects to the Practical MLOps cluster

The Practical MLOps cluster explains each subsystem in depth — MLflow production setup, end-to-end pipeline architecture, model serving, monitoring and retraining, and CI/CD/CT. This projects cluster shows how those subsystems connect into one traceable lifecycle and how that lifecycle maps to AWS, Azure, and GCP.

If you need the full technical explanation of a single component, link to the Practical MLOps page. If you need to see how a complete project works end-to-end or on a specific cloud, you are in the right place.

  • Practical MLOps hub: /mlops/practical-mlops/
  • MLflow production setup: /mlops/practical-mlops/mlflow-production/
  • End-to-end pipeline architecture: /mlops/practical-mlops/end-to-end-pipeline/
  • Model serving: /mlops/practical-mlops/model-serving/
  • Monitoring and retraining: /mlops/practical-mlops/model-monitoring-retraining/
  • CI/CD/CT: /mlops/practical-mlops/mlops-ci-cd-ct/

Sources and last verification

Guided implementation with instructor review

The MLOps course provides maintained learning repositories, guided lab sequences, instructor code review, and the real AWS SageMaker capstone scope supported by current account access.

  • Build the end-to-end project with instructor-reviewed labs
  • Execute the SageMaker capstone with current account access
  • Debug production failure modes with guided feedback
Explore the MLOps course