A practical guide to human-in-the-loop distillationThis blog post presents practical solutions for using the latest state-of-the-art models in real-world applications and distilling their knowledge into smaller and faster components that you can run and maintain in-house.
Applied NLP Thinking: How to Translate Problems into SolutionsWe’ve been running Explosion for about five years now, which has given us a lot of insights into what Natural Language Processing looks like in industry contexts. In this blog post, I’m going to discuss some of the biggest challenges for applied NLP and translating business problems into machine learning solutions.
How to uncover and avoid structural biases in evaluating your Machine Learning/NLP projectsPyData LondonThis talk highlights common pitfalls that occur when evaluating ML and NLP approaches. It provides comprehensive advice on how to set up a solid evaluation procedure in general, and dives into a few specific use-cases to demonstrate artificial bias that unknowingly can creep in.
Prodigy in 2023: LLMs, task routers, QA and pluginsWe have made a ton of new updates in Prodigy this year with v1.12, v1.13, and v1.14 releases. So we decided to write a post about them.
Against LLM maximalismLLMs are not a direct solution to most of the NLP use-cases companies have been working on. They are extremely useful, but if you want to deliver reliable software you can improve over time, you can't just write a prompt and call it a day. Once you're past prototyping and want to deliver the best system you can, supervised learning will often give you better efficiency, accuracy and reliability.
