Conversational AI at large scale

There has been a quantum leap in the performance of conversational AI. From speech recognition to machine translation and language understanding, deep learning made its mark. However, scaling and productizing these breakthroughs remains a big challenge. Yishay Carmiel shares techniques and tips on how to take advantage of large datasets, accelerate training, and create an end-to-end product.

There has been a quantum leap in the performance of conversational AI. From speech recognition to machine translation and language understanding, deep learning made its mark. However, scaling and productizing these breakthroughs remains a big challenge—while implementing a cutting-edge algorithm using an open source deep learning framework is certainly doable, creating a scalable product that encapsulates these amazing algorithms and runs them at large scale is difficult. Yishay Carmiel shares techniques and tips on how to take advantage of large datasets, accelerate training, and create an end-to-end product. Topics include: