Project Review and Looking Forward

Chapter 13 of this book was an adventure to the cutting edge of technology, as you integrated OpenAI and Azure OpenAI into your Power BI project using the FAA Wildlife Strike data. Over the course of this book, you have walked through a project for ingesting raw data, transforming and prepping the data, building a BI solution for analytics, using analytics to discover data features for predictive ML models, building those ML models, scoring new data with the ML models, and adding new, robust descriptive and summarization capabilities to the solution using LLMs from OpenAI and Azure OpenAI. In Chapters 12 and 13, you augmented and improved text using OpenAI, too. All of this was accomplished, from end to end, using Power BI.

In this chapter, you will review what you have accomplished throughout the book and project, revise important key concepts, and then discover some suggestions for future iterations of the project and your career as a data professional...

Lessons learned from the book and workshop

At the beginning of this book, you started with the objective to provide your leadership with tools to enable interactive analysis of the FAA Wildlife Strike data, in order to find insights about factors that influence incidents and make predictions about future possible wildlife strike incidents and associated costs. The primary goal of your project, predicting the future impact of FAA Wildlife Strikes, required building out Power BI ML models. Through the chapters of this book, you walked through the process, using content from the Packt GitHub repository, to plan out and implement an end-to-end project. Everything in this project was achieved using tools within Power BI or that were integrated with Power BI. A high-level summary of the artifacts you created in Power BI is shown in Figure 14.1:

Figure 14.1 – Summary of the artifacts created in Power BI in this book

The primary technical training for this...

Looking forward

Now that you’ve progressed from defining requirements, to transforming and exploring raw source data from the FAA Wildlife Strike database, to building ML models, to working with OpenAI and Cognitive Services, what’s next? Do you publish the solution for your stakeholders and users, give yourself a high five, and move on to the next project? Do you continue to iterate on this project for added value? Do you build upon the lessons from this book and dive deeper into ML and OpenAI?

Next steps for the FAA Wildlife Strike data solution

The examples used in this book only scratch the surface of the different use cases that could be built out using the FAA Wildlife Strike database. In your real-world work, most people will never need to use FAA Wildlife Strike data. Even so, there is value in brainstorming possible next steps so that you can incorporate such thinking into your solutions. Without adding new data, a few examples might include, but not be...

Summary

In this chapter, you reviewed all of the lessons from the book and the affiliated workshop. Over the course of 13 chapters, you have traversed the intersection of BI, ML, AI, and OpenAI by way of a single use case. Beginning with raw FAA Wildlife Strike data, you built an end-to-end workshop using the affiliated Packt GitHub repository. Particular attention was given to Power BI ML, and then integration with OpenAI and Azure OpenAI. The next steps and options for working with the FAA data were discussed. Options for future career plans were then covered in the context of the content in this book. In an age of rapid technological evolution, hopefully the architectural design process and strategies covered in this book will help you long after these tools have evolved beyond the state in which they were covered in this book.

Learning about Power BI ML in this book will hopefully open the door for you to find whatever is next in your career. It may be as a Power BI professional...