About Me

I’m a senior machine learning specialist at AWS. I received my M.S. in mathematics with an emphasis in data science from Tarleton State University and previously was a senior data scientist and solutions architect at TechnipFMC.

For a more detailed view of my professional history, you can check out my resume. If you like aesthetic resume templates, get the template I used for free.

Open source projects and contributions

agent-evaluation, a generative AI-powered framework for testing virtual agents
amazon-bedrock-samples: custom Amazon CloudWatch dashboard for Bedrock apps
bedrock-vscode-playground, a VS Code extension that enables developers to explore and experiment with Large Language Models (LLMs) available in Amazon Bedrock
vscode-zettel-archive, a VS Code extension that supports note-taking motivated by the Zettelkasten system
sagemaker-scikit-learn-container, Amazon SageMaker’s pre-built Scikit-learn framework container where I fixed an issue regarding outdated boto3 and botocore versions which blocked usage of new AWS APIs from 2021 and 2022.
sagemaker-python-sdk, the official Amazon SageMaker Python SDK where I fixed a model registry problem with some of SageMaker’s pre-built containers.
aws-samples: sagemaker-feature-store-real-time-recommendations, a machine learning workshop given at re:Invent 2021 walking attendees though creating and deploying a real-time recommendation engine leveraging Amazon SageMaker Feature Store as a core component of the solution
aws-samples: enhanced-pyspark-processor, a class override to enable local mode for SageMaker Processing with Spark
aws-samples: ml-lineage-helper, a library around SageMaker ML Lineage Tracking extending ML Lineage to end-to-end ML lifecycles, including additional capabilities around Feature Store groups, queries, and other relevant artifacts
amazon-sagemaker-examples, a contribution to AWS’s official amazon-sagemaker-examples repo in GitHub exploring the flexibility and practicality of Script Mode in SageMaker
covid19, a repo containing code written to scrape COVID-19 data, do analyses, and generate content for the endcoronavirus.org website
ml-model-lambda-microservice, deploy a scikit-learn model in AWS using Lambda and API Gateway
ZenML, a custom data science and deep learning Python library
stokepy, a stochastic models Python library
atari pong ai, an AI that learned to play pong via reinforcement learning
markov twain text generator, a Markov model implementation of a Mark Twain chatbot
self-assembly, a particle physics simulation to gain insight about how particles self-assemble
binning optimization of categorical levels, a statistical framework that decides which categorical levels should be kept before a model is built
denoising dirty documents Kaggle competition, an OCR algorithm to clean up coffee-stained, sun-spotted, and wrinkled pages that have been digitized

Certifications

AWS

Stanford

Divide and Conquer, Sorting and Searching, and Randomized Algorithms

Deep Learning Specialization, deeplearning.ai

TensorFlow in Practice Specialization, deeplearning.ai

External publications

AWS Machine Learning Blog

Conference talks & media appearances

Toronto Summit 2024

Accelerate development and delivery of FMs on Amazon SageMaker [AIM303]: I was a speaker for a session that provided FMOps best practices and tooling

New York Summit 2024

Accelerate development and delivery of FMs on Amazon SageMaker [AIM308]: I was a speaker for a session that provided FMOps best practices and tooling

re:Invent 2023

Deliver high-performance ML models faster with MLOps tools [AIM305]: I was a speaker for an advanced-level MLOps workshop covering MLOps tools on AWS that help you automate and standardize testing, managing, deploying, and monitoring hundreds to thousands of machine learning models.

re:Invent 2022

Implementing MLOps to deliver high-performing ML models faster [AIM308]: I was a speaker for an advanced-level MLOps workshop covering MLOps tools on AWS that help you automate and standardize testing, managing, deploying, and monitoring hundreds to thousands of machine learning models.

MLOps World 2022

Implementing MLOps Practices on AWS using Amazon SageMaker: I was a speaker for an advanced-level MLOps presentation and workshop covering machine learning operations with Amazon SageMaker.

DC Summit 2022

AWS On Air: Using SageMaker Pipelines to build repeatable ML workflows
Improving your machine learning in production with MLOps [AIM401]: I was the speaker for an advanced-level machine learning chalk talk where I walked attendees through core strategies and best practices for improving their machine learning operations.

re:Invent 2021

Real-time recommendations using Amazon SageMaker Feature Store [AIM415]: I was the speaker (and content developer) for an expert-level machine learning workshop walking 100+ attendees through creating and deploying a real-time recommendation engine leveraging Amazon SageMaker Feature Store as a core component of the solution. For more details, see the workshop description:

Data scientists apply feature transformation on organizations’ raw data to generate aggregated features for their machine learning algorithms. These aggregated features are critical for real-time applications, such as fraud detection, real-time recommendations, and personalization. However, serving these types of features for real-time predictions in production poses a difficult problem. In this workshop, learn how to use Amazon SageMaker Feature Store to make personalized recommendations more responsive and improve viewer experience.

Explore, analyze, and process data using Jupyter notebooks [AIM324]: I was the speaker (and content developer) for an advanced-level machine learning chalk talk where I walked attendees through newly-released SageMaker Studio features, specifically exploring and connecting to different data sources directly from the notebook and using that connection to analyze and process data, then train and deploy a model - all without leaving your notebook.