Concluding Remarks

As metaknowledge has a central role in many approaches discussed in this book, we address the issue of what kind of metaknowledge is used in different metalearning/AutoML tasks, such as algorithm selection, hypeparameter optimization, and workflow generation. We draw attention to the fact that some metaknowledge is acquired (learned) by the systems, while other is given (e.g., different aspects of the given configuration space). This chapter continues by discussing future challenges, such as how to achieve better integration of metalearning and AutoML approaches, and what kind of guidance could be provided by the system when configuring metalearning/AutoML systems to new settings. This task may involve (semi-)automatic reduction of configuration spaces to make the search more effective. The last part of this chapter discusses various challenges encountered when trying to automate different steps of data science.