Summary
In this chapter, you learned about the meaning and importance of reproducibility in machine learning modeling. You also learned about data and model versioning, which help us to develop more reliable and reproducible models and data analysis results. Next, you learned about the different tools and Python libraries you can use to version your data and models. With the concepts and practices introduced in this chapter, you are ready to ensure reproducibility in your machine learning projects.
In the next chapter, you will learn about techniques you can use to avoid and eliminate data drift and concept drift, which constitute two differences between the behavior of models before and after deployment.
