Wonderfully readable step by step guide through the most contemporary world of AI. Highlights the usage of data management to better our world. The example using aircraft safety prediction using available metrics is profoundly interesting and suggestive of an unlimited positive future for Microsoft data management methods. The author presents the material in a most readable and likeable style.
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Packt is a leading publisher of technical learning content with the ability to publish books on emerging tech faster than any other.
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From the Publisher
What makes this book different from other Power BI machine learning books?
Unlike similar resources that feature training data, the data used for this book is real data from the Federal Aviation Administration (FAA), providing an example of real-world data.
This book also features a unique step-by-step GitHub repo that follows the examples in the chapters, which enables you to recreate the content from those readings for hands-on learning. At the same time, your journey is structured so that you can progress from raw data to ML and OpenAI, learning via a real-world end-to-end data experience.
How has your experience helped you write this book?
I have worked on many different real-world data projects with hundreds of customers, in many different industries. Based on my experience, two common threads among data professionals are the need for time and easily accessible training materials.
Data professionals are frequently pressured to deliver new content on aggressive timelines, yet the rapidly changing technologies in the data space require frequent continuous training. One of my goals for this book was to create easily consumable workshop-style content that will help readers skill up quickly, on-demand, and in a format that is enjoyable to read.
How can Power BI Machine Learning and OpenAI help you to leverage Power BI and OpenAI together?
Power BI professionals are usually accustomed to working with structured data that exists in rows and columns. This book provides examples on how you can expand your existing use cases to include generative AI for added value.
I’ve chosen the workshop format with the GitHub repo to help readers implement these examples for hands-on experience, and with both OpenAI and Microsoft Azure OpenAI. These examples will also contain architectural patterns to help you leverage OpenAI for summarization and text generation for numerous instances in historical databases.
How does this book cater to both beginners and more experienced practitioners in the field of data science and machine learning?
The Power BI ML tools in this book are no-code SaaS tools that are designed for beginners. The book is a great way for BI professionals to learn about ML and related use cases, while experienced data science professionals will learn more about Power BI and some of the tools that can be used for reporting and prepping data for data science projects.
The end-to-end workshop-style approach with an affiliated Git repo will give you hands-on experience with ML and help you build a project that you can show in interviews and on resumes.
About the author
Unique book promises to take us on an impactful learning journey at the intersection of BI, ML, & AI
This unique book promises to take us on an impactful learning journey at the intersection of business intelligence, machine learning, and artificial intelligence. The author has crafted an applied, hands-on approach using real-world FAA data that allows Power BI professionals to skill up on ML and AI in a practical workshop format. Rather than a technical manual on the specifics of Power BI ML, the goal is a holistic educational experience about bringing together BI, ML, and AI to prepare for the future of data analytics. This book fills an important gap by using a full project walkthrough to teach Power BI users about incorporating ML in the context of actual workflows. The inclusion of OpenAI provides a cutting-edge capstone to the journey. The complimentary GitHub repository allows readers to recreate the entire end-to-end solution along the way. The author has struck a good balance between providing beginner-friendly ML training for BI professionals using Power BI ML tools and setting appropriate expectations around production-level ML rigor. Cautioning against over-reliance on no code BI ML tools for highly accurate use cases is wise guidance. Overall, this book appears well-positioned to advance analytics skill sets for BI professionals on ML and AI while also allowing ML experts to gain valuable BI project perspective. The blend of technical training and conceptual discussion about bringing together different data disciplines sets it apart from more narrowly specialized analytics books. Following the preface, one anticipates an educational experience that will provide immediately applicable new skills combined with thought leadership about data convergence. For BI professionals looking to expand their capabilities or ML experts seeking cross-disciplinary learnings, this book represents an incredibly valuable opportunity. The author has laid out a compelling vision and roadmap in the preface for a book that should empower readers to flourish in the future of analytics.
