The science of scaling large language models is an interesting field of research. For me it was shaped by combining aspects of graph theory with the transformer architecture to develop a new powerful pruning method.

I am working on the science aspects of improving and developing Amazon SageMaker automatic model tuning, a fully-managed platform enabling data scientists and developers to easily build, train, and deploy machine learning models at scale.

In my time at the Professional Services Emerging Technologies team I worked on the identification of topic trends in tech with relevance to the organization using Natural Language Processing.I worked with Huggingface Transformers as well as variety of different conventional NLP models such as Latent Dirichlet Allocation and Term Frequency - Inverse Document Frequency.I also got the chance to shadow a ProServe customer project, getting insights into the daily life of a consultant.

I worked on customer demand forecasting for specific AWS service groups supporting Account Managers in prioritizing their accounts. In my time in the Independent Software Vendor (ISV) Account management team I learned a lot about data engineering working on large data sets with tool such as jupyter notebooks and pandas.For the Machine Learning part of the project I worked with supervised learning models such as Extreme Gradient Boosting (XGB).