Teaching Computers to Read

Building Natural Language Processing (NLP) and artificial intelligence (AI) solutions that deliver ongoing business value is not straightforward. "Teaching Computers to Read" provides clarity and guidance on how to design, develop, deploy, and maintain NLP solutions that address real-world business problems. The code companion provides hands-on emphasis of the best practices and approaches in the book.

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I wrote this book to support both early career professionals (or students) as well as leaders who are either leading or collaborating with technical teams. In the book, we discuss the main challenges and pitfalls encountered when building NLP and AI solutions. We also outline how technical choices interact with (and are impacted by) data, tools, the business goals, and integration between human experts and the artificial intelligence (AI) solution.

 

The best practices covered  do not depend on cutting-edge modeling algorithms or the architectural flavor of the month (we see you, LLMs and Agents). Instead, provide practical advice for NLP solutions that are adaptable to the solution’s specific technical building blocks.

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Through providing best practices across the lifecycle of NLP development, this handbook will help teams to use critical thinking to understand how, when, and why to build NLP solutions, what the common challenges are, and how to address or avoid those challenges. These best practices help organizations deliver consistent value to their stakeholders and deliver on the promise of AI and NLP.