MLOps (Machine Learning Operations) helps solo developers streamline the entire machine learning lifecycle—from data preparation and model training to deployment, monitoring, and continuous improvement. By combining version control, automation, CI/CD, containerization, and cloud deployment, MLOps makes it easier to build reliable, scalable, and production-ready AI applications. Even without a large team, developers can leverage tools such as Git, Docker, MLflow, FastAPI, GitHub Actions, and cloud platforms to automate workflows, track experiments, and manage models efficiently. Learning the fundamentals of MLOps empowers solo AI developers to deploy machine learning solutions faster, reduce manual effort, and maintain high-quality, reproducible projects throughout their development journey.