NLP for Product Managers

Recently, natural language processing has become the focus of interest across many industries. Still, most practitioners find it challenging to understand the key concepts behind this powerful technology. In this ebook, we want to take a deep dive into how to apply modern NLP to your own use cases. We also show you how to use pre-built language models when developing an NLP application.

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A Guide to Practical NLP for Business Applications

State of Modern NLP

Today, NLP is not comparable to what existed even ten years ago. Transformer-based systems have taken the lead in semantic search, sentiment analysis, question answering, and other use cases involving NLP. As the result, product teams have started to apply these models to real-world enterprise applications and never looked back.

Industry Applications for NLP

Thanks to model sharing, everyone can use the same powerful, pre-trained language models. Anybody can now apply these models' astonishing capabilities of understanding natural language to their own data.

NLP Implementation Cycle

Due to the modular nature of NLP systems and the nuances of the language models involved, it is useful to break down the process of implementing any modern NLP application into a series of steps. We like to think of this process as a fully-fledged product lifecycle.

How to Succeed

Designing and implementing an NLP backend isn't an easy task. By following this guide, you're on a good path to a system that truly provides value to your users. To make sure that you really get there, learn about the final tips to keep in mind.