Applied NLP Thinking: How to Translate Problems into Solutions

2021-06-19 · Source: Explosion · Developer tools and consulting for AI, Machine Learning and NLP - Explosion.ai · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, quick

Summary

Explosion, a company with five years of operational experience, has accumulated substantial insights into the practical application of Natural Language Processing within industrial settings. This upcoming blog post aims to delve into the most significant hurdles encountered in applied NLP, specifically focusing on the intricate process of translating abstract business problems into concrete, implementable machine learning solutions. The discussion will draw upon Explosion's extensive experience to illuminate the complexities involved in bridging the gap between business requirements and technical NLP implementations, offering a perspective rooted in real-world industry contexts.

Key takeaway

For NLP Engineers or ML Leads tasked with deploying solutions, understanding the inherent challenges in translating business needs into machine learning problems is crucial. You should anticipate complexities in bridging the gap between high-level business objectives and specific NLP model requirements. Prepare to deeply analyze the problem space to ensure your technical approach directly addresses the core business challenge, rather than just a superficial symptom.

Key insights

Applied NLP faces challenges in translating business problems into ML solutions.

Principles

• Industry context is key for NLP.

Topics

• Applied NLP
• Industry Contexts
• Business Problem Translation
• Machine Learning Solutions
• NLP Challenges

Best for: NLP Engineer, Machine Learning Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Explosion · Developer tools and consulting for AI, Machine Learning and NLP - Explosion.ai.