Premier Analytics Consulting at the Midwest SAS® Users Group (MWSUG) 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.

Artificial Intelligence (AI) continues to reshape business, technology, science, and research by enabling systems to learn from data, automate workflows, and support more adaptive decision-making. This paper presents a practical roadmap for understanding AI through the progression from machine learning (ML) foundations to modern generative and agentic AI systems. It introduces AI as a broad field and machine learning as a data-driven approach to AI, then examines supervised learning for predictive analytics on labeled data, unsupervised learning for pattern discovery in unlabeled data, and generative learning for representation learning, synthesis, reasoning, and content generation. The paper introduces key algorithms, architectures, use cases, model hyperparameters, and practical considerations such as overfitting, underfitting, and model evaluation to support applied implementation across industry settings. It concludes by connecting deep learning with modern generative model architectures, including encoder models, decoder-only large language models, and encoder-decoder models, to the emerging shift toward agentic AI systems that combine models with retrieval, tools, APIs, and workflow orchestration to support practical industry use cases in 2026, 2027, and beyond.

Clinical AI is moving beyond standalone copilots and experimental chatbots, creating a need for secure, traceable systems that can support clinical development and regulatory submission workflows through 2027 and beyond. This paper presents a practical roadmap for applying AI to focused use cases, including protocol and statistical analysis plan review, SDTM and ADaM mapping support, TLF development, CSR contextualization, and submission-readiness checks. It connects these use cases to core components of the clinical AI stack, including AI agents, retrieval-augmented generation, vector search, tool integration, and proprietary or locally deployed language models. Implementation patterns are presented primarily in Python, with connections to R and SAS® Viya® workflows. This paper presents a staged roadmap for integrating AI workflows that assist clinical and regulatory teams while maintaining QA/QC and human-in-the-loop review.

Developers, data scientists, and analysts are increasingly leveraging open-source tools and libraries to integrate with and enhance their existing data engineering and analytical 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 processing, visualizing, and analyzing data alongside SAS procedures. This presentation demonstrates how to access and use Python libraries in Viya runtimes; understand data-handling in Python and SAS; build Python scripts with reusable methods that import, process, and analyze data; and execute those Python methods to export Pandas DataFrames as SAS datasets.