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Accelerating NLP in Production with Transfer Learning

Transfer Learning and Knowledge Distillation have accelerated the adoption of deep learning models for production workloads, particularly large language models (LLMs) that can be fine-tuned on upstream tasks that are business-specific with very little data.

Additionally, the NLP field has seen an explosion of toolsets that enable rapid prototyping and deployment of complex architectures with minimal code and general development requirements.

These high-level wrappers that abstract away the complexities are democratizing NLP and allow organizations to deliver more value, rapidly! In this talk, we explore some of the business requirements that a team of only two ML engineers has been able to deliver at SBG and some lessons learned.

How Matter produces an outside-in view on companies’ sustainability performance

Matter is a fintech company, specializing in sustainability screening of investment portfolios. This talk will be about how Matter utilizes NLP to...