Modern data science/analysis workbench

Building an analyst workbench is the best way to bring structure and utility to the data science effort. This is especially true today with the rise of new classes of machine learning algorithms that implement stochastic data processing beyond legacy supervised (and unsupervised) machine learning models or the second generation of deep learning approaches of the past. Generative AI algorithms are being integrated into production daily, and their data engineering necessities are not fully being taken into account. Missing these necessities results in poor outcomes such as proprietary sourced training data being output directly by the model (see the New York Times lawsuit against OpenAI {https://packt-debp.link/sFVXOJ}) rather than the targeted semantically summarized output that the model intended. How knowledge graphs can be used to produce embeddings that drive semantic correctness in generative AI models is a technique the knowledge engineer...