There's huge value to be gained from AI and machine learning when put to work strategically, but these techniques can be daunting, especially if you are new to them. The good news is, you don't need to be a data scientist to make good choices about much of what matters most to success with AI. From recognizing the business settings appropriate for machine learning to understanding what is needed in architecture and infrastructure to handle data and model management, you can make a difference, and this Buyer's Guide to AI and Machine Learning helps to show you how.
Whether you are just getting started to build your data science teams or you are an experienced practitioner looking for tips on how to optimize the effectiveness of your AI and machine learning systems, you should find useful material in this book such as:
How to identify key points in a business that can benefit from AI and machine learning
The basic aspects of AI and machine learning from model training and evaluation to why you might use deep learning or transfer learning
How to evaluate the total cost of ownership (TCO) of a machine learning system
A quick survey of machine learning tools and tips on how your choice of infrastructure can affect their usefulness through open data access
The role of emerging technologies such as Kubernetes and dataware in having a big impact on the overall success of AI and machine learning, especially in multi-tenant systems.
A convenient guide to the platform capabilities that are most needed for successful AI and machine learning systems in practical business settings.