Why You Need a Sandbox Separate from Your ML Pipeline to Create Successful Experiments

Published 2/27/2023 7:08:58 PM
Filed under Machine Learning

Machine learning operations (MLOps) is a rapidly evolving field that is transforming the way businesses approach data science. One of the most critical aspects of MLOps is the ability to create and test experiments quickly and efficiently. While it may be tempting to use your production ML pipeline as a testing ground, it's essential to have a separate sandbox environment for several reasons.

Avoiding costly mistakes

The first reason to have a separate sandbox environment is to avoid costly mistakes. In a production environment, you have a lot at stake, including data, time, and money. If you make a mistake in your experiment, it could lead to significant financial losses or even legal consequences. Having a separate sandbox environment allows you to test your experiments without the risk of damaging your production environment.

Increasing productivity

The second reason is productivity. Getting data from a production data lake into your MLOps environment takes time and can be expensive. A sandbox environment that’s connected to your production data lake is a great solution to provide data scientist with the freedom they need to quickly iterate on a machine-learning experiment.

Maintaining consistency

The third reason to have a separate sandbox environment is to maintain consistency. In a production environment, you need to ensure that everything is working as expected. If you start making changes to your ML pipeline, it could impact the reliability of your results. By keeping your sandbox environment separate, you can experiment with new ideas without worrying about affecting the consistency of your production environment.

Building a sandbox environment

Building a sandbox environment that is connected to your production data lake is critical to the success of your machine-learning experiments. The sandbox environment should feature limited compute power, which allows data scientists to quickly iterate on models and experiment with different approaches.

It's important to note that the sandbox environment should not be connected to your production environment. This ensures that any mistakes made during the experimentation process won't affect your production environment. Keep in mind that the sandbox environment is for testing purposes only and should not be used for production workloads.

To build a sandbox environment, you'll need a separate container in your production data lake where you can copy data from the production data lake or create new datasets. Using a separate container with separate permissions help manage any data governance risks that arise from this use of data.

Finally, we recommend that you allow the use of python or R notebooks in the sandbox environment as we’ve found that notebooks are great for quick experiments that don’t have to run in production.

Scaling your experiments

There comes a time when your experiment outgrows the sandbox. There are a few tell-tale signs it’s time to move your experiment out of the sandbox into the MLOps environment:

  • The amount of data being used becomes too large for the sandbox environment to handle.
  • The complexity of the experiment requires more computing power than is available in the sandbox environment.
  • The experiment has been successful in the sandbox environment and is ready to be moved into the production ML pipeline.

When your machine learning experiment outgrows the sandbox, it's time to move it into the MLOps environment. However, before doing so, it's essential to work on code quality and data preprocessing logic used in the experiment. Writing unit tests for the codebase and automating the data preprocessing logic can help ensure that the experiment is robust and scalable in a production environment.

It's also crucial to version the dataset, code, hyperparameters, and model used in the experiment. This makes it easier to reproduce the results and track changes over time. Adopting a version control system, such as Git for source code and an experiment tracking solution like MLFlow for tracking your training runs, models, and hyper parameters, can help you manage versions quickly and efficiently.


In this article we emphasized the importance of having a separate sandbox environment for machine learning experiments instead of using a production ML pipeline. We’ve listed three reasons for this: avoiding costly mistakes, increasing productivity, and maintaining consistency.

Next we covered guidelines on building a sandbox environment and scaling experiments, including code quality, data preprocessing, and version control. This is by no means a complete guideline of course, but you can find many guides online on how to set up a machine-learning environment.

Let us know in the comments how you're optimizing the safety and efficiency of your machine-learning experiments!