Summary

In this chapter, we provided a brief overview of what is covered in this book. First, we discussed the need for real data and the main problems usually associated with collecting and annotating large-scale real datasets. Then, we saw that synthetic data presents a clever solution that elegantly mitigates most of these problems and issues. Second, we mastered the main approaches to generating diverse and realistic synthetic data. Third, we explored various case studies and learned about the main issues and limitations of synthetic-data-based ML solutions.

Essentially, you have learned how to overcome real data issues and how to improve your ML model’s performance. Moreover, you have mastered the art of meticulously weighing the pros and cons of each synthetic data generation approach. You have also acquired best practices to better leverage synthetic data in practice.

Now, as we approach the end of our learning journey with synthetic data for ML, you are well-equipped...