AutoML makes machine learning accessible for everyone! Machine Learning is hard since developing well-performing machine learning pipelines requires a lot of expertise, time and manual tuning. AutoML automates this development process by using latest optimization methods to efficiently search for well performing solutions. In this talk, we will cover how to move from manually constructing and tuning machine learning pipelines to using efficient hyperparameter optimization algorithms and full AutoML using the popular open-source Auto-sklearn library. Auto-sklearn is a drop-in replacement for any scikit-learn estimator and is developed by the ML Lab of the University of Freiburg. More specifically, you’ll learn the following: What is AutoML and for what can you use it? How does Auto-sklearn work? How can you use it? This talk assumes basic understanding of machine learning and statistics. The main target audience are data scientists and domain experts using machine learning. The talk will be designed such that anyone with a basic understanding of machine learning pipelines in scikit-learn and the Python language would be able to understand the concepts and to use our tool.