In the evolving landscape of data science, advanced computational tools are crucial for driving innovation and efficiency. This tutorial introduces the Bayesian Back End (BayBE), an AI-assisted open-source experimental planner developed by Merck KGaA, which utilizes Bayesian Optimization and machine learning to smartly streamline experimental workflows in the low-to-no-date regime. From chemical reactions to biological assays to coffee machine settings - with BayBE users can find optimal configurations in an iterative manner, which is anyway the main working mode of many experimentalists.

We will start the first part with a brief introduction to Bayesian Optimization, highlighting its principles and advantages in experimental design. Following this, we will showcase BayBE's unique features, including elegant categorical encodings and advanced capabilities like active learning, transfer learning or Pareto optimization.

In the second part, we explain some of our code and test design choices that went into the open-source Python package baybe. This will include learnings about our built-in (de-)serialization engine, CI/CD, advanced hypothesis tests, autodocumentation and open-source tools BayBE is built on.

The final part will comprise of a hands-on tutorial. We will look at representative problems and guide potential users from formalization of the problem to performing the iterative loop to analyzing the results including an assessment of parameter relevance.