Creating Value With Artificial Intelligence: Lessons Learned from 10 yrs of Building AI Products and Overcoming Data, Adoption, and Engineering Challenges
This book bridges the gap between the commoner’s world and the AI technical world. This easy read book is suitable for anyone who wants to dig deeper into the practical aspects and explore the value of AI and build intelligent products for real-world use cases. You will learn
- Where can AI or intelligent machines add Value? - What real-world problems can be solved and what problems are harder to solve using existing AI algorithms, - A brief explanation of the most applicable algorithms and for which use cases they are most suitable, - How to engineer and architect AI systems, - How to overcome some of the major challenges with gathering the data, developing the product, and making users adopt the product.
The above steps are explained through use cases taken from banking, insurance, energy, sales, healthcare, and other sectors.
Almost all the knowledge and use cases shared in this book have been gained during my many years (almost 10 years actually) spent working and researching various AI-related products and are based primarily on my personal experiences. However, this is not a course book on Artificial Intelligence (AI), or a comprehensive literature review on AI or its use cases
IF YOU CANNOT AFFORD THE COST OF THE BOOK, PLEASE CONNECT WITH THE AUTHOR VIA LINKEDIN AND HE WILL LET YOU KNOW ABOUT THE NEXT FREE PROMOTION.
If you're looking for a comprehensive, a few hundred pages long study about how to use ML and AI in business - it's not this one. But if you want to: - get a short and interesting introduction into the topic, that will be a good starting point for broadening your knowledge, and - get an overview of what Machine Learning is, how different algorithms work and how to choose the right one depending on the product you're creating (or rather: a problem you want to be solving),
then "Creating value with AI" is a right book for you.
It is an easily digestible overview of ML's product/ business use for non-engineers, with interesting case studies. I am familiar with the basics already, thus I missed more numbers, references and more detailed tech explanations. If you're new to the topic, you will most likely enjoy the read and find it valuable.