Accelerate with BERT: NLP Optimization Models

There are two primary difficulties when building deep learning natural language processing (NLP) classification models. Our ability to build complex deep learning models that are capable of understanding the complexity of language has typically required years of experience across these domains. The harder your problem, the more diverse your output, the more time you need to spend on each of these steps. Data collection is burdensome, time-consuming, expensive, and is the number one limiting factor for successful NLP projects. Preparing data, building resilient pipelines, making choices amongst hundreds of potential preparation options, and getting "model ready" can easily take months of effort even with talented machine learning engineers.