Building a ML Serving Platform at Scale for Natural Language Processing
xNLP driven AI is a fast-growing technology domain with diverse applications in customer engagement, employee collaboration, marketing, and social media. Does having an accurate model by itself mean, that we have a successful product, service, or solution? No! The most difficult phase begins after that. We still have the following challenges to solve. How do we Create a sellable service around this model? How to Scale this service to handle millions of inference requests, in a cost-effective manner? How to build automated deployment pipelines for software and models? How to add security, privacy, manageability, and observability for the service? How to track model drift and analyze model performance? In this session, we will discuss these questions and explore answers. We will go through the unique challenges that NLP serving poses and the solutions and best practices to overcome them.
This session was recorded at ODSC West, 2020, and present by Kumaran Ponnambalam
Workshop Overview and Author Bio
Goals and Session Agenda
An AI Architecture Framework & Contact Centers
Real-time Publishing & Stream Processing
Streaming NLP and NLU, and Orchestration
Kumaran Ponnambalam
Kumaran Ponnambalam
Big Data & Data Science & Analytics Leader | Cisco