Training a machine learning model is only one aspect of developing a reliable AI application. In production, you must also evaluate models, track experiments, validate data, monitor performance, automate deployments, and manage failures. MLOps projects are essential for AI engineers aiming to build production-ready systems beyond the notebook environment.
In this article, I’ll share 10 practical projects covering ML monitoring, LLM evaluation, CI/CD, automated retraining, and deployment.
MLOps Projects for AI Engineers
MLOps is the set of practices that answers this question: how do you automate pipelines, experiment tracking, model versioning, data validation, drift detection, monitoring, CI/CD, retraining, deployment, and rollback?
Practical experience is essential for developing these skills. The following ten projects are organized in the sequence a real system is typically built.
- Evaluate an AI Agent with Python
- LLM Cost & Latency Tracker with Python
- Setting Up a CI/CD Pipeline for LLM Applications
- ML Model Monitoring Dashboard With Python
- LLM Evaluation Dashboard With Python
- ML Pipeline That Retrains a Model When Performance Drops
- Custom Evals for an LLM Pipeline
- Implementing a Self-Healing Data Pipeline
- Evaluation Pipeline for Your LLM App
- Dockerize an AI Agent
Preparing for AI/ML Interviews?
Building production-ready ML systems is valuable, but it is equally important to articulate your technical decisions and problem-solving approach during interviews. I recommend Cracking Your First AI/ML Interview as a resource if you are preparing for your first AI/ML role.
The Takeaway
Begin with experiment evaluation and model monitoring, then advance to automated pipelines, CI/CD, deployment, and rollback. For LLM-focused roles, prioritize evaluation, cost tracking, and agent reliability.
These projects can be developed using Python and open-source tools, with complexity adjusted to your experience level.
Go beyond completing tutorials by integrating two or three projects into a single workflow. For example, evaluate a model, monitor its performance, trigger retraining, validate the new version, and roll back if necessary.
Thank you for reading this article on 10 MLOps Projects for AI Engineers. For additional insights on AI and machine learning, feel free to follow me on Instagram.
