Teaching data science can be challenging partly due to a diverse student population and the difficulty of providing a hands-on coding experience on complex topics. To address these challenges, we introduce a software package that facilitates active learning in the form of coding exercises during lectures. This approach provides a much-needed hands-on experience in courses, where students have diverse academic backgrounds and need to program advanced and/or complex solutions. Utilizing JupyterHub, a popular cloud-based technology, the technology enables in-class exercises with personalized feedback from the instructor. We report a classroom experience of using the technology for the first time in a graduate-level Machine Learning course, consisting of a mix of Data Science and Computer Science students. We found that, to a great extent, it was possible to conduct complex in-class exercises within 10-15 minutes of class time. The instructor noted he was able to provide meaningful personalized and group feedback to students, and was able to understand their abilities and challenges better. Students felt that the experience provided valuable hands-on practice, helped them figure out their coding mistakes, and prepared them better for homework assignments.