About the talk
As competition increases, brands are evolving to make customer service a critical differentiator. The rule of the day is to provide faster and smarter contact center experiences. Delivering this level of service starts with having well-trained agents that know your product, but increasingly AI can be used to boost these agents – making them even more effective.
At the same time, the emergence of transformers has seeded this AI opportunity, increasing the potential for conversational AI. And libraries of pre-trained transformers like HuggingFace promise to democratize access to these models for these use cases. But at the same time, these pre-trained transformers have their limitations, especially when applied to real-world use cases. So how can modeling teams fine-tune pre-trained transformers to make them more impactful on their real-world modeling problems?
In this talk, Mindtree deep learning architects Bhanu Prakash and Sulata Patra will discuss techniques to refine pre-trained transformers from the HuggingFace library in an attempt to boost accuracy and accelerate inference time. They’ll focus their talk on automating conversation summarization through the development of a custom version of the BART summarization model from the HuggingFace library. In the course of this discussion, they’ll share technical details on how they implemented solutions like SigOpt to enable this refinement through a guided experimentation and hyperparameter optimization process. And they’ll provide real-world examples for how pre-trained BART performance compares to refined BART – and how this makes a big difference for their business.
Bhanu is an accomplished machine learning and software architect with over 20 years of experience in the consumer electronics, semiconductor, and automotive industries. He has extensive experience with short-range wireless networks, Linux, firmware, machine learning and deep learning. Currently, Bhanu draws on this expertise to address a variety of problems in conversational AI at Mindtree. Within Mindtree, he is the go-to person for the most difficult and pressing technical challenges that require data science and optimization solutions. He is passionate about fostering the next generation of technical and architects, serving as a mentor and advisor to emerging engineers across these domains.View the profile
Sulata is a results-driven leader and IT expert with over ten years of experience in manufacturing, finance, data science, and engineering. Since 2016, she has worked as a data scientist for Mindtree to address a wide variety of AI problems, using Python, Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Power BI, and Databricks. Most recently,s he led efforts to build conversational AI to enhance customer support at Mindtree. Sulata is passionate about using various AI models to solve different problems., and has expertise in data modeling, data mining, analytics, and artificial intelligence.View the profile
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