Stu Sztukowski

Python developers have more options than ever for working with SAS® Viya®. Whether you prefer VS Code, JupyterLab, notebooks or another workflow, there's no single "right" way to build. The best approach depends on how you work, your team's requirements and the problems you're trying to solve. During my SAS Innovate 2026 session, I shared six different ways to run Python in SAS Viya.

In my first blog on hyperparameter autotuning, I used a cake analogy to show how to use hyperparameter autotuning with Optuna and the sasviya.ml package in Python to improve detecting Higgs Bosons in a particle accelerator. SAS Viya Workbench now supports hyperparameter autotuning in SAS code with a variety of different machine learning models, helping you achieve greater accuracy with less code. Remember how using Optuna was like asking Gordon Ramsay to critique our cake?

It’s done. It’s finally done. After many caffeine-fueled late nights and countless hours of feature engineering, you’ve built the ultimate model. But the journey isn’t over. Not quite yet. There’s one thing left for you to do: get that model into production and let your ModelOps team take the reins. That’s where SAS Model Manager shines.