Building Complete State of the art Natural Language Processing
summary
Three production tools built on Spark NLP enable fast end-to-end projects: a Python NLU library that gives one-line load and predict access to over 4,000 prepackaged models (200+ languages) and embeddings, backed by Spark NLP’s 10M+ downloads and 31% practitioner adoption; an on‑premises Annotation Lab that is free, unlimited, used in dozens of projects for 2+ years and offers auto-training with active learning, versioning, role-based projects and analytics; plus an NLP Server providing no-code UI and API access to the model hub.
AI-written summary · about
chapters (8)
- 0:00Introduction and motivation
- 1:39Building NLP projects with free tools
- 6:34NLU Python library overview
- 15:55NLU advanced features and models
- 19:58Annotation Lab introduction
- 22:28Annotation Lab features and workflow
- 27:46Annotation Lab analytics and deployment
- 29:32Closing and call to action
related talks
33mState of the art natural language understanding at scale
30mTurbocharging State-of-the-art Natural Language Processing on Ray
55mDemystifying Natural Language Processing across several industry verticals
06mAdvancements in Natural Language Processing (NLP) and Text Analytics
12mUnlocking the Power of NLP: Techniques in K8s-Native Applications
45mArchitecture of an NLP Deployment
50mDeep Learning and NLP with Spark
26mA Practical Introduction to Productionizing NLP Models