With the release of Couchbase 7.0, integration with Python User-Defined Functions (UDFs) is now possible through Couchbase Analytics, enabling organizations to process data at scale and extract valuable insights in real-time. Advanced analytics domains like predictive analytics rely on processing large amounts of data near real-time, making this integration particularly useful. A seamless pipeline has been created from Python-based machine learning models to Couchbase Analytics, allowing users to apply external algorithms to their NoSQL data. The process involves training a machine learning model, creating a Python library, packaging and deploying the library, importing bucket documents for the UDF to analyze, writing User-Defined Functions, and invoking these functions in predictive queries within Couchbase Analytics. This integration promises to be an effective way of extracting valuable information from data without compromising on performance or efficiency.