Machine learning is a method of analyzing data using an analytical model that is built automatically from training data. The goal is to enable the algorithm to improve over time as more data points are fed into it. Machine learning has two distinct steps: training and operationalization. Training involves exploring the data set to find patterns, while operationalization involves deploying the model to a production system where it runs to score new data and returns results to users. To accomplish these steps, various tools such as data ingestion tools, data cleansing libraries, development frameworks, testing platforms, and deployment environments are used. SingleStore is a distributed database platform that excels at doing calculations typically found in machine learning models, making it an ideal environment for storing training data and operationalizing algorithms. It meets key requirements for effective operationalization, including fast calculations, scalability, compatibility with existing libraries, and powerful programming languages, allowing organizations to build efficient ML applications. SingleStore can be used in various ways, including as a fast service layer that stores raw data and serves results, pipelines that execute the ML scoring algorithm on incoming data, or even within the database itself using a language to encode the algorithm. By leveraging these approaches, organizations such as Thorn and Nyris.io have successfully implemented machine learning applications with improved performance and efficiency.