In 1949, The Star newspaper predicted that computers would one day help with income-tax calculations, but this was considered speculation at the time. Today, top-of-the-line laptops can handle astronomical calculations at lightning speed, a fact that has become reality. To deploy machine learning models in production, developers need to consider ease of development and tuning/training at scale. Python is the de-facto programming language for machine learning workloads, thanks to its PyData ecosystem and ease of use. Developers prefer to customize their environments using conda or virtual environments to match their staging or production environment. To achieve this setup, two distinct environments can be created with required dependencies, allowing developers to seamlessly switch between them. When model training requires more compute-intensive resources than a laptop can offer, an existing cluster can be extended to meet those requirements. The Ray.io platform is recommended for three reasons: it provides a simple client to connect to an existing cluster, leverages native libraries for ML-specific tasks, and allows developers to use their favorite IDE or development tools while still scaling up to a cluster. Ray Tune is used for tuning and training at scale, providing state-of-the-art algorithms and easy-to-use APIs. It can be used with popular tuning libraries like Hyperopt and Optuna, giving developers the tools and techniques to do model training and tuning at scale. Ray Train allows for distributed data-parallel deep learning training using PyTorch or TensorFlow models, offering composability and interoperability with Ray Tune. Both Ray Tune and Ray Train are interoperable, allowing developers to use them together when needed. The MLflow platform is recommended for tracking experiments and results, providing a fluent API and robust integrations with both Ray Tune and Ray Train. Developers can log metrics, parameters, and artifacts using callbacks like MLflowLoggerCallback, making it easy to view results in the MLflow UI. By following these considerations, developers can ensure their machine learning models are deployed, served, and inferred at scale.