Engine Works

mbarone

Here in the Data Science department at my company, we build all kinds of models—predictive, prescriptive, and everything in between. Whether it's powering AI pipelines, optimizing business decisions, or forecasting future trends/client behavior, our work spans a wide range of use cases and technologies. Each model is its own little universe, built at different times with different tools, and often with very specific package requirements. And as anyone who's wrangled Python environments knows, keeping all those dependencies happy can be... a bit of a circus.

Why I Use Separate Python Environments for Each Model

When building models, one of the most persistent challenges faced is maintaining them over time. As projects evolve and new models are developed, the dependencies and package versions they rely on can quickly become a source of conflict. To avoid this, we’ve adopted a practice that has proven invaluable: creating a distinct Python environment for each individual model.

The core reason is simple: version isolation. When models are built at different times, they often rely on different versions of packages. For example, a model developed in 2023 might depend on a specific version of scikit-learn, while a newer model built in 2025 could require an updated version of the same package. If both models were to share a single environment, updating the package for the newer model could inadvertently break the older one. By assigning each model its own environment, we ensure that its dependencies remain frozen in time.

This approach not only protects the integrity of each model but also simplifies deployment and reproducibility. Each environment becomes a self-contained unit, tailored specifically to the model it supports. No surprises, no conflicts—just clean, reliable execution.

How I Do It

To keep things clean and conflict-free, a dedicated Python environment for each model is created right inside its own project directory. This way, the environment lives alongside the model pipeline, isolated from other projects.

Here’s a peek at what that looks like in practice: a model folder with its own environment inside. This setup ensures that only the model in question uses that environment, and nothing else can interfere with it. If you peek inside, you’ll find the usual suspects: Lib, Include, and of course, the Scripts folder:

A Closer Look Inside the Environment

Once the environment is created and lives quietly inside the model’s folder, if you look inside Scripts, you’ll see the python.exe file—the Python interpreter that runs the model code. This is the version-specific, environment-bound Python executable that ensures your model uses exactly the setup it was built with.

From Preprocessing to Execution: Alteryx in Action

In our Alteryx workflows, native Alteryx tools and sometimes user-created macros play the main role in prepping the data before any Python code runs. We use Control Containers to organize and manage the preprocessing steps—everything from pulling raw data to cleaning, transforming, and enriching it. Once the data is ready, a clean .csv file is output for the model’s Python consumption.

Then comes the handoff.

The next Control Container kicks off the model execution using Alteryx’s Run Command tool. This tool allows us to call a .py script directly from the workflow, using the isolated Python environment we created earlier. It’s a simple but powerful bridge between Alteryx and Python, letting us keep preprocessing and modeling tightly integrated within Alteryx, while maintaining a clean separation of processes:

Looking at the Configuration of the Run Command Tool

This tool is configured to call a .bat file that lives in the same directory as the model and its environment. The .bat file opens the correct python.exe from the model’s isolated environment and runs the associated .py script. It’s a clean handoff that keeps everything modular and version-safe:

Configuration (just the Command for now):

.bat file:

Capturing Python Console Messages: The Command Arguments

One of the most useful parts of the Run Command tool configuration is the Command Arguments field. This is where we capture the output from the Python console window as the script runs. Any errors, warnings, or custom messages printed from your code will show up here, making it an essential tool for debugging and monitoring.

To make this work, the Command Arguments field includes two parts:

The path to the .bat file (again).
The path to a log file where the output will be written (you can name it whatever you like; here, I named it “Python_Console_Log.log”.

These two paths are separated by a > symbol, which redirects the console output to the log file. To capture both standard output and error messages, we end the line with 2>&1. Here's what the full command looks like:

Finishing the Configuration: Silent Execution and Output Capture

To keep things smooth, we configure the Run Command tool to execute minimized and silently. This prevents any pop-up windows or interruptions during workflow execution—especially useful when running models in production or automated pipelines.

The Python code is designed to output key results like model scores, metrics, or predictions, which are written to a file (often a .csv or .txt file). Alteryx then reads this output via the outgoing anchor of the Run Command tool, and passes it downstream for further analysis, reporting, or decision-making:

What the Python Console Log Looks Like

Here’s a look at a sample log file. You’ll see everything from environment setup messages to packages loaded, models invoked, and any custom logging you’ve built into your script. It’s a simple but powerful way to keep tabs on what’s happening under the hood:

Wrapping It All Up

Managing models across time, tools, and teams can get messy fast. By isolating Python environments per model, integrating clean data prep in Alteryx, and using the Run Command tool to execute scripts reliably, we’ve built workflows that are modular, maintainable, and fairly future-proof.

This setup gives each model its own space to thrive, free from dependency conflicts and version chaos. It also makes debugging easier, keeps logs transparent, and ensures that downstream processes run smoothly every time.

Key Takeaways

If you’re building and maintaining multiple models, especially over time, this approach can save you a ton of headaches. Here’s the quick recap:

One model, one environment: No version conflicts, no surprises.
Alteryx for preprocessing: Clean, visual workflows that output ready-to-use data.
Run Command + .bat file: Executes your model in its own sandbox.
Console logs: Capture everything your script says—for better debugging and transparency.
Output files: Feed model results right back into your Alteryx workflow.

If you’re tired of dependency drama or mysterious model failures, give it a try—you might just find yourself wondering how you ever worked without it!