Emily Webber
Principal Machine Learning Specialist Solutions Architect at Amazon Web Services
Washington, District of Columbia, United States
Summary
👤
Senior🎓
Top SchoolEmily Webber is a Principal Machine Learning Specialist Solutions Architect at AWS with 11 years of experience designing and deploying production ML systems and big-data pipelines for enterprise customers. She blends hands-on engineering—shown by contributions fine-tuning GPT-2 and T5 translation notebooks for SageMaker—with practitioner teaching as an adjunct professor and ML mentor to startups. Emily’s background spans economic policy ML at the Federal Reserve to explainable-AI product design, giving her a rare mix of rigorous quantitative training and customer-facing solution design. A published author, keynote speaker, and inventor based in Washington, DC, she also brings a humanist perspective—practicing meditation and nonprofit leadership—that informs pragmatic, ethically minded AI deployments.
11 years of coding experience
5 years of employment as a software developer
Bachelor's Degree, International Finance, 3.96, Bachelor's Degree, International Finance, 3.96 at Prescott CollegeManheim Township High SchoolMS Computational Analysis and Public Policy, Machine Learning for Economic Policy, MS Computational Analysis and Public Policy, Machine Learning for Economic Policy at The University of Chicago
Stackoverflow
312reputation
36kreached
7answers
13questions
Github Skills (21)
python10
machine-learning10
sagemaker10
trainings10
gpt10
nlp10
aws-lambda9
amazon-s39
aws-s39
deep-learning9
s3-bucket9
jupyter-notebook9
tensorflow8
deploying8
hashtable6
Programming languages (4)
Github contributions (5)
aws/studio-lab-examples
Nov 2021 - Apr 2022
Example notebooks for working with SageMaker Studio Lab. Sign up for an account at the link below!
Role in this project:
ML Engineer
ML EngineerContributions:105 commits, 8 PRs, 21 pushes in 4 months
Contributions summary:Emily's commits focus on adding and modifying a Jupyter Notebook designed for machine translation. The notebook uses the T5 model from Hugging Face to perform English to Spanish translation on COVID-19 health service announcements. The user is likely exploring and finetuning machine translation models.
signtraininglinkawssagemaker-studio
aws-samples/amazon-sagemaker-architecting-for-ml
Jan 2019 - Aug 2020
Materials for a 2-day instructor led course on applying machine learning
Role in this project:ML Engineer
ML EngineerContributions:126 commits, 5 PRs, 84 pushes in 1 year 6 months
Contributions summary:Emily primarily worked on fine-tuning a GPT-2 model within the context of the Amazon SageMaker examples repository. Their contributions involved creating a lambda function to clean SageMaker resources, as well as developing and integrating code for processing data from the repository and then using it to fine-tune a GPT-2 model. The user also defined scripts, requirements and utilized S3 for data storage and model output.
sagemakerdata-scienceleddaymachine-learning
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