Premier Analytics Consulting at the Western Users of SAS® Software (WUSS) 2026 Conference

This paper presents a practical framework for building reliable, reproducible, and collaborative data science workflows using Git, GitHub, Data Version Control (DVC), and MLflow. It begins by introducing Git as the foundation for tracking code changes and GitHub as a collaboration layer for shared repositories, branching strategies, pull requests, and team-based development. DVC is then presented as an extension to version control that enables datasets, intermediate outputs, and analytical artifacts to be tracked, compared, and restored alongside code without storing large files directly in Git repositories. The paper demonstrates strategies and techniques for meaningful commit practices, managing work-in-progress (WIP) safely, reducing merge conflicts, and maintaining structured project histories across collaborative environments. Building on this foundation, MLflow is introduced as a lightweight experiment tracking and model management layer for machine learning workflows in Python, allowing teams to record training runs, fine-tuning parameters, evaluation metrics, model artifacts, and performance comparisons during iterative development. Together, Git, GitHub, DVC, and MLflow provide an integrated open-source ecosystem for managing code repositories, versioning data, and tracking model experiments across collaborative data science, machine learning, and analytics projects.

Generative AI is rapidly reshaping how organizations search, reason over, and contextualize information, yet deploying these systems on private, confidential, and sensitive data introduces distinct architectural, performance, and governance challenges. This paper presents a practical roadmap for the 2026 enterprise generative AI stack, with emphasis on agentic AI architectures built on retrieval-augmented generation (RAG), vector embeddings, and secure model deployment. Core concepts including encoding, similarity search, and vector databases are introduced to explain how knowledge is stored, retrieved, and reused across AI agents, alongside API-driven patterns that enable tools for search, reasoning, and contextualization. The discussion contrasts proprietary large language models (LLMs) and open-source small language models (SLMs), highlighting trade-offs in output quality, performance, and deployment, while examining how ecosystems such as Hugging Face support localized inference and domain-specific adaptation. Implementation considerations are presented primarily in Python, with extensions to R and SAS® Viya®, focusing on secure, reproducible analytics workflows. This paper concludes with prompt engineering strategies and ethical considerations for responsible generative AI use on sensitive enterprise data in the life sciences, pharmaceutical, and healthcare domains.

Data scientists, statistical programmers, machine learning engineers, and researchers are increasingly leveraging a growing number of open-source tools, libraries, and programming languages that can enhance and seamlessly integrate with their existing data workflows. One of these integrations, built into SAS® Viya®, is its pre-configured Python runtime integration, PROC PYTHON, that gives SAS programmers access to Python's open-source data science libraries for wrangling and modeling structured and unstructured data alongside the validated procedures provided in SAS. This paper demonstrates how to install and import external Python libraries into their SAS Viya sessions; generate Python scripts containing methods that can import, process, visualize, and analyze data; and execute those Python methods and scripts using SAS Viya's PYTHON procedure. By integrating the added functionalities of Python's libraries for data processing and modeling with SAS procedures, SAS programmers can enhance their existing data workflows with Python's open-source data solutions.