MLOps Projects · Ideas and selection
MLOps Project Ideas: Beginner, Intermediate and Production Projects
Choose an MLOps project with realistic scope, acceptance criteria, and evidence requirements. Progress from reproducibility through monitoring and rollback.
What MLOps project should you build?
Start with a reproducible training pipeline — version your data, pin your dependencies, and prove two runs produce the same result. Then add experiment tracking and a model registry. Move to automated pipelines, model serving, and drift detection. Finally, implement CI/CD, rollback, and retraining. The best project is one you can complete and evidence, not one that covers every tool. Build one layer at a time and connect it to the previous one.
- Difficulty
- beginner
- Environment
- Multi-platform
- Last verified
- 2026-08-19
How to choose an MLOps project
The best MLOps project is one you can complete and prove. Do not start with a multi-cloud distributed training platform. Start with reproducibility, add automation, then add deployment, monitoring, and rollback. Each project should produce inspectable evidence — a run log, a deployment revision, a monitoring dashboard, or a rollback test.
Choose by your current level and what you want to prove. If you are starting, prove reproducibility. If you can train models, prove automation. If you can deploy, prove observability. If you can monitor, prove recovery.
Beginner: Versioned dataset and reproducible training
You have a training script that works on your machine but produces different results when re-run.
- Outcome
- Two runs from the same commit and data version produce identical results.
- Difficulty
- beginner
- Infrastructure
- Local machine or small cloud VM
- Prerequisites
- Python, Git, Basic ML training
Suggested stack
Acceptance criteria
- Data is versioned with DVC and tracked in Git
- Dependencies are pinned in a requirements file
- Training script runs from CLI, not notebook
- Two runs produce the same metric values
Required evidence
- DVC tag showing data version
- Two run logs with identical metrics
- Requirements file with pinned versions
Common failure: Skipping data versioning and claiming reproducibility because code is in Git.
Beginner: MLflow experiment and registry workflow
You train models but cannot compare runs or track which model version is approved.
- Outcome
- Training runs are logged in MLflow with parameters, metrics, and artifacts. Models are registered with version and approval status.
- Difficulty
- beginner
- Infrastructure
- Local machine or small cloud VM
- Prerequisites
- Reproducible training script, Python
Suggested stack
Acceptance criteria
- MLflow tracking server logs all training runs
- Parameters, metrics, and artifacts are recorded
- Model is registered in the Model Registry
- Model version has approval status (staging or production)
Required evidence
- MLflow UI screenshot showing run comparison
- Registered model with version number
- Approval status recorded
Common failure: Logging parameters but not artifacts, making it impossible to reproduce the model from the run.
Next: Automated training pipeline
Intermediate: Automated training pipeline
Training requires manual steps — running notebooks, copying files, and hoping nothing changes.
- Outcome
- A pipeline orchestrates data prep, training, and evaluation without manual intervention.
- Difficulty
- intermediate
- Infrastructure
- Local Docker or small cloud VM
- Prerequisites
- Reproducible training, MLflow tracking
Suggested stack
Acceptance criteria
- Pipeline has discrete stages with input and output contracts
- Pipeline runs end-to-end without manual steps
- Failed stages are logged with enough context to diagnose
- Pipeline can be triggered by CLI or schedule
Required evidence
- Pipeline DAG execution log
- Stage-level success and failure logs
- Output artifacts for each stage
Common failure: Building a pipeline that works once but fails on the second run because of state leakage.
Next: Model serving API with load evidence
Intermediate: Model serving API with load evidence
Your model works in a notebook but has never served predictions behind an API.
- Outcome
- A model is served behind a REST API with health checks, latency measurement, and a known revision.
- Difficulty
- intermediate
- Infrastructure
- Local Docker or small cloud VM
- Prerequisites
- Registered model, Docker basics
Suggested stack
Acceptance criteria
- API serves predictions with a /health endpoint
- Latency is measured at different concurrency levels
- Deployment has a known image tag and revision
- Prediction test verifies correct outputs
Required evidence
- Load test report with p50/p95/p99 latency
- Health check response
- Prediction test results
Common failure: Serving a model without concurrency or latency testing, then discovering it crashes under real traffic.
Next: Drift detection and retraining candidate
Intermediate: Drift detection and retraining candidate
Your model is deployed but you have no way to know when it starts degrading.
- Outcome
- A monitoring system tracks prediction distribution and data drift, and creates a retraining candidate when thresholds are breached.
- Difficulty
- intermediate
- Infrastructure
- Local Docker or small cloud VM
- Prerequisites
- Deployed model, Monitoring basics
Suggested stack
Acceptance criteria
- Monitoring tracks prediction distribution and input drift
- Alert rules notify when thresholds are breached
- Retraining candidate is created on drift signal
- Candidate must pass evaluation before promotion
Required evidence
- Monitoring dashboard with drift metrics
- Alert rule configuration
- Retraining candidate creation log
Common failure: Setting up drift detection without a baseline, so you cannot tell what 'drifted' means.
Next: CI/CD model promotion and rollback
Production: CI/CD model promotion and rollback
Model deployment is manual and there is no rollback path when something goes wrong.
- Outcome
- A CI/CD pipeline automates evaluation, approval, and deployment. Rollback to the previous revision is tested.
- Difficulty
- advanced
- Infrastructure
- Kubernetes cluster or cloud container service
- Prerequisites
- Automated pipeline, Model serving, Monitoring
Suggested stack
Acceptance criteria
- CI/CD pipeline triggers on code or model changes
- Evaluation gates block promotion on quality regression
- Deployment is automated with a known revision
- Rollback to previous revision is tested and documented
Required evidence
- CI/CD pipeline execution log
- Rollback test result
- Deployment revision history
Common failure: Implementing CI/CD without a rollback test, then discovering during an incident that the previous revision is broken.
Next: End-to-end MLOps project
Production: AWS SageMaker lifecycle
You want to implement the full MLOps lifecycle on AWS using managed services.
- Outcome
- A SageMaker project with training, model registry, endpoint deployment, and CloudWatch monitoring.
- Difficulty
- advanced
- Infrastructure
- AWS cloud
- Prerequisites
- AWS account, MLOps fundamentals, CI/CD basics
Suggested stack
Acceptance criteria
- SageMaker training job executes with logged metrics
- Model is registered with version and approval
- Endpoint reaches InService and serves predictions
- CloudWatch tracks endpoint telemetry
Required evidence
- Training job log
- Endpoint InService confirmation
- Prediction test results
Common failure: Assuming SageMaker handles everything without configuring IAM, networking, and monitoring explicitly.
Next: End-to-end MLOps project
Production: Streaming validation and alerting
Batch monitoring is too slow — you need to detect data quality issues in near-real-time.
- Outcome
- A streaming validation pipeline that checks data quality as it arrives and alerts on violations.
- Difficulty
- advanced
- Infrastructure
- Cloud streaming platform or local Kafka
- Prerequisites
- Data validation basics, Streaming basics
Suggested stack
Acceptance criteria
- Data is validated as it streams through the pipeline
- Invalid data is quarantined, not silently passed
- Alerts fire on validation failures
- Validation rules are versioned and tested
Required evidence
- Streaming pipeline execution log
- Quarantined data example
- Alert configuration
Common failure: Validating data but not alerting, so failures are discovered days later.
Next: Multi-environment release contract
What evidence belongs in an MLOps portfolio
A portfolio project without evidence is a claim, not a proof. For each project, include the artifacts that demonstrate the system works:
- Run logs or pipeline execution logs showing successful end-to-end execution
- Model evaluation report with metrics against a real dataset
- Deployment evidence — endpoint health check, prediction test, or load test result
- Monitoring dashboard showing latency, drift, or prediction quality
- Failure log or limitation section showing what went wrong and how it was handled
- Rollback test or recovery procedure
Common project shortcuts that weaken credibility
These shortcuts make a project look complete but prove nothing:
- Deploying a model without a prediction test — the endpoint is up but you have not verified it works
- Adding monitoring without a baseline — you cannot detect drift if you do not know what normal looks like
- Implementing CI/CD without a rollback test — the pipeline deploys but you cannot recover from a bad deployment
- Using a notebook as a pipeline — it runs once but is not reproducible or automatable
- Listing tools without showing artifacts — a stack list is not evidence
Turn one project into an end-to-end system
You do not need six separate projects. You can build one project and extend it through each maturity level. Start with a reproducible training script. Add MLflow tracking. Add a pipeline. Add serving. Add monitoring. Add CI/CD and rollback. Each extension adds evidence and moves you up the maturity ladder.
The end-to-end MLOps project page shows how all these layers connect into one traceable lifecycle.
Project selection matrix
Choose by difficulty and what you want to prove. Each brief above has full acceptance criteria and evidence requirements.
| Outcome | Difficulty | Environment | Evidence |
|---|---|---|---|
| Reproducible training | beginner | Local | Officially documented |
| Experiment tracking | beginner | Local | Officially documented |
| Automated pipeline | intermediate | Docker | Officially documented |
| Model serving | intermediate | Docker | Officially documented |
| Drift detection | intermediate | Docker | Officially documented |
| CI/CD and rollback | advanced | Kubernetes | Officially documented |
| AWS SageMaker | advanced | AWS | Partially tested |
| Streaming validation | advanced | Kafka | Officially documented |
Sources and last verification
Practical MLOps Authority Cluster
School of Core AI · 2026-08-18
Supports: Cluster 1 owns the subsystem explanations referenced by project briefs.
https://schoolofcoreai.com/mlops/practical-mlops/Last verified: 2026-08-19 · scai-execution
Build these projects with guided labs
The course provides maintained learning repositories, guided lab sequences, instructor code review, and the real AWS SageMaker capstone scope.
- Build each project with instructor-reviewed labs
- Execute the SageMaker capstone with current account access
- Produce reviewed portfolio artifacts by the end of the track