With Embedded Machine Learning, MarkLogic will run queries more efficiently and scale autonomously based on workload patterns. With autonomous elasticity, for example, MarkLogic can use models of infrastructure workload patterns to automatically adjust the rules that govern data and index rebalancing.

Embedded Machine Learning reduces complexity and increases automation of various steps in the data curation process. For example, with MarkLogic’s Smart Mastering feature, machine learning will augment the rules-based mastering process so that records are mastered with more accuracy, and models continue to improve as more data is processed—all with less human involvement.

For data scientists, it’s now simpler to just do the work of training and executing models right inside MarkLogic, where we can handle almost every part of the architecture and process. This includes data processing/curation, and the model engineering to build, train, execute and deploy the model.