Low-Code AI

Chapter 9. Next Steps in Your AI Journey

Throughout the course of this book, you have learned how data can drive decision making in your business with an enterprise ML workflow, how to understand your data with an eye toward building ML models, and what tools are available for building ML models. You have discovered how to use AutoML to train your regression and classification models, how to create custom low-code models using SQL in BigQuery ML, how to create custom code models using the scikit-learn and TensorFlow framework, and then finally how to improve your custom model performance with further feature engineering and hyperparameter tuning. Hopefully, you have found this journey to be equally enlightening and enjoyable. For many, that should be more than enough to enable you to infuse ML into your problem-solving processes.

For others, this is only the beginning of a longer journey into ML and AI. This chapter explores where to go next. You will learn about other important topics in data science and ML operations (or MLOps). You will also be pointed toward many wonderful resources to grow your knowledge beyond this book.

Going Deeper into Data Science

There is no universally agreed-upon definition for data science or a data scientist. A decent approximation of such a definition could be that data science is the discipline that uses various tools from other disciplines to extract insights from datasets. These various tools come from other areas such as mathematics, statistics, ...