Summary

The knowledge that you have gained in this book about ML, data science and data engineering, MLOps, and the ML life cycle applies to any other ML platforms as well. You have not only gained important insights and knowledge about running ML projects in Kubernetes but also gained the experience of building the platform from scratch. In the later chapters, you were able to gain hands-on experience and wear the hats of a data engineer, data scientist, and MLOps engineer.

While writing this book, we realized that the subject is vast and that going deep into each of the topics covered in the book may be too much for some. Although we have touched upon most of the components of the ML platform, there is still a lot more to learn about each of the components, especially Seldon Core, Apache Spark, and Apache Airflow. To further your knowledge of these applications, we recommend going through the official documentation pages.

ML, AI, and MLOps are still evolving. On the other hand...