Machine learning projects require the integration of various components, from experimentation to deployment, and tools like MLflow and BentoML facilitate this process by offering experiment tracking and model serving solutions. MLflow is utilized for logging and tracking models, capturing experiment metrics, and storing model artifacts, while BentoML focuses on the production phase by managing model deployment, input validation, adaptive batching, and creating APIs for model serving. This tutorial details the implementation of these tools using a classification model trained on the Iris dataset, demonstrating how to set up an MLflow tracking server, log metrics and models, and subsequently use BentoML for model registration, versioning, and deployment as a production-ready API. It further explores input data validation and adaptive batching to enhance performance and reliability, while also addressing deployment strategies for larger teams and standardizing workflows. The seamless integration of MLflow and BentoML ensures that data scientists can efficiently transition their models from development to reliable production environments, with options for containerization and cloud deployment available through BentoML's infrastructure.