Most machine learning portfolios stop at a notebook with 94% accuracy. Hiring managers have seen that a hundred times, and it says nothing about whether you can run a model in production. That gap is exactly what MLOps projects close. Companies such as Amazon, Google, and Microsoft run dedicated ML platform teams to keep models healthy after launch, and they look for engineers who have done the same work on a smaller scale. Below are 12 MLOps projects, ordered from beginner to advanced, with the tools and the career skill each one builds.
What Is MLOps?
MLOps, short for machine learning operations, is the set of practices that automates and monitors the full lifecycle of an ML system: data preparation, training, testing, deployment, and monitoring. It applies DevOps ideas like CI/CD to models and data, so releases are repeatable, and failures are caught early.
Google Cloud’s architecture guidance says practicing MLOps means advocating automation and monitoring at every step of building an ML system. It describes three maturity levels: manual process (level 0), ML pipeline automation (level 1), and CI/CD pipeline automation (level 2). Many ML operations teams sit at level 0 or 1, so engineers who can reach level 2 stand out. If an interviewer asks what is MLOps, the short answer is DevOps for models and data.
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Why Build MLOps Projects? The MLOps Benefits Employers Look For
The 2015 Google paper on hidden technical debt in machine learning systems showed that the model code is a small box inside a much larger system of data pipelines, configuration, serving, and monitoring. That larger system is what MLOps projects let you practice.
The MLOps benefits teams care about most are easy to demonstrate in a portfolio:
- Reproducible training, so any result can be rebuilt from versioned data and code.
- Faster and safer releases through automated tests and gated deployments.
- Early drift detection, so accuracy problems surface before customers notice.
The market reflects this. Fortune Business Insights estimates the global MLOps market at about USD 4.39 billion in 2026, growing toward USD 89.91 billion by 2034, though estimates vary by research firm. In India, Glassdoor data puts the average MLOps engineer salary near ₹16 lakh a year, with a typical range of roughly ₹8 to ₹22 lakh.
How to Choose the Right MLOps Projects
Pick MLOps projects by the maturity level you want to prove, not by how flashy the dataset looks. Use one simple tabular dataset, such as telecom churn, for projects 1 to 8 so your work compounds into a single end-to-end system. Every project needs a Git repo, a README, an architecture diagram, and one measurable result, like p95 latency or retraining time.
Beginner MLOps Projects: Learn the Core Workflow
These beginner MLOps projects teach the habits every production team relies on: track, version, serve, and test.
1. Experiment Tracking for a Churn Model with MLflow
Train a churn classifier with scikit-learn or XGBoost and log parameters, metrics, and artifacts to MLflow across at least 20 runs. Compare runs in the UI, then register the best model in the Model Registry with an alias such as champion. MLflow 3.x also adds tracing and prompt versioning for GenAI apps, so the skill carries over. Skill proved: reproducibility.
2. Data and Model Versioning with DVC
Put your dataset and model files under DVC, store them in an S3 or Google Cloud Storage bucket, and define a dvc.yaml pipeline with prepare, train, and evaluate stages. Then reproduce any past experiment by checking out an older Git commit. Skill proved: data lineage, which banking and healthcare teams care about.
3. Containerised Model API with FastAPI and Docker
Wrap the registered model in a FastAPI service with Pydantic validation, a health endpoint, and a prediction endpoint. Package it in a slim Docker image, load test it with Locust, and push it to Docker Hub or Amazon ECR. Skill proved: serving a model as a dependable service, a core ML operations task.
4. Automated Tests and CI for ML Code with GitHub Actions
Add unit tests for feature code, data checks with Great Expectations, and a GitHub Actions workflow that lints, tests, trains on a small sample, and builds the image on every pull request. Fail the build if accuracy drops below a threshold. Skill proved: treating models like software.
Intermediate MLOps Projects: Automate the Pipeline
Once the basics work, these intermediate MLOps projects connect them into pipelines that run without you.
5. Training Pipeline with Airflow, Prefect or Kubeflow
Turn the notebook into a scheduled pipeline: ingest data, validate it, train, evaluate, and register the model only if it beats the current champion. Airflow and Prefect are easy starting points, while Kubeflow Pipelines is a Kubernetes-native option for containerised steps. Skill proved: orchestration and Google’s level 1 maturity.
6. Feature Store with Feast
Define entities and features in Feast, serve historical features for training and low-latency features from Redis for inference. Compare predictions with and without the store to see training-serving skew firsthand. Skill proved: consistent features across training and serving, a common source of silent production bugs.
7. Model Monitoring and Drift Detection with Evidently
Log every prediction, then use Evidently to compare live data with a reference window and report data drift. Export metrics to Prometheus, chart them in Grafana, and alert when drift crosses a threshold. Simulate drift by shifting one feature’s distribution. Skill proved: knowing when a model has quietly gone stale.
8. CI/CD with Continuous Training
Connect projects 4, 5, and 7: when the drift alert fires, a pipeline retrains the model, runs evaluation gates, and promotes the new alias for a canary release. Google calls this continuous training, and level 2 maturity adds CI/CD for the pipeline itself. Skill proved: safe, automated releases.
Advanced MLOps Projects: Production Scale and Cloud
Advanced MLOps projects show you can run machine learning where traffic, cost, and uptime actually matter.
9. Kubernetes Model Serving with KServe
Deploy your model on Kubernetes with KServe, using kind or minikube locally and EKS or GKE in the cloud. Configure autoscaling, a canary rollout that sends 10 percent of traffic to the new version, and a one-command rollback. Skill proved: scalable serving and release strategies.
10. Cloud MLOps on AWS SageMaker or Vertex AI with Terraform
Rebuild the pipeline on a managed platform: SageMaker Pipelines or Vertex AI Pipelines for training, a managed endpoint for serving, and Terraform to provision everything as code. Add least-privilege IAM roles and a budget alert. Skill proved: cloud ML operations and infrastructure as code.
11. Real-Time Fraud Detection with Streaming Features
Stream transactions through Kafka, compute rolling features with Spark Structured Streaming or Flink, and score each event with a low-latency model service. Track p95 latency against a target such as 100 milliseconds and add shadow deployment to test new models silently. Skill proved: real-time inference, valued in fintech.
12. LLMOps Capstone: Production RAG App with Tracing and Evaluation
Build a retrieval-augmented generation app on a vector database such as Qdrant or pgvector. Add MLflow tracing, automated evaluation with Ragas or LLM judges, prompt versioning, and per-request cost tracking, then gate releases on faithfulness scores. Skill proved: LLMOps, which GenAI teams increasingly ask for.
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MLOps Projects at a Glance
Use this table to plan your order of attack across all 12 MLOps projects.
| # | Project | Level | Key Tools |
| 1 | Experiment tracking | Beginner | MLflow, scikit-learn |
| 2 | Data versioning | Beginner | DVC, S3 or GCS |
| 3 | Model API | Beginner | FastAPI, Docker |
| 4 | ML CI pipeline | Beginner | GitHub Actions, Great Expectations |
| 5 | Training pipeline | Intermediate | Airflow, Prefect, Kubeflow |
| 6 | Feature store | Intermediate | Feast, Redis |
| 7 | Drift monitoring | Intermediate | Evidently, Prometheus, Grafana |
| 8 | CI/CD with retraining | Intermediate | GitHub Actions, MLflow Registry |
| 9 | Kubernetes serving | Advanced | KServe, Kubernetes |
| 10 | Cloud MLOps with IaC | Advanced | SageMaker or Vertex AI, Terraform |
| 11 | Real-time fraud scoring | Advanced | Kafka, Spark or Flink |
| 12 | LLMOps RAG capstone | Advanced | MLflow tracing, Ragas, pgvector |
How to Showcase Your MLOps Projects
Recruiters skim, so make your MLOps projects easy to verify. Avoid the usual mistakes:
- Notebook-only repos with no tests, no pipeline, and no monitoring.
- Hard-coded secrets or file paths that break on a fresh machine.
- No numbers. Always report latency, retraining time, or drift results.
Instead, add a one-command setup with Docker Compose or a Makefile, an architecture diagram, and a short demo video.
Build Production ML Skills with Scaler
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Also Explore These Projects to Build Your Portfolio
Frequently Asked Questions
What are MLOps projects?
MLOps projects are hands-on builds that take a model through tracking, versioning, deployment, monitoring, and retraining, the way production teams do.
What is MLOps in simple terms?
What is MLOps? It is short for machine learning operations: automating, deploying, and monitoring models so they stay reliable in production.
Which MLOps projects are best for beginners?
Start with MLflow tracking, DVC versioning, and a FastAPI plus Docker API; these MLOps projects teach the core workflow.
What are the main MLOps benefits?
The main MLOps benefits are reproducible training, faster and safer releases, and early drift detection that protects accuracy.
How long do MLOps projects take to complete?
Beginner MLOps projects take about a weekend each, while advanced ones like KServe or streaming can take two to three weeks.
Is MLOps different from DevOps?
Yes. DevOps ships code, while ML operations also versions data, retrains models, and monitors drift.
Do MLOps projects help you get hired?
Yes, MLOps projects with CI/CD, monitoring, and deployment show production readiness, and Glassdoor lists the average in India at near ₹16 lakh a year.
