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
- 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Amazon SageMaker’s pre-built Scikit-learn framework container where I fixed an issue regarding outdated boto3 and botocore versions which blocked usage of new AWS APIs from 2021 and 2022.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-examplesrepo 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
Deep Learning Specialization, deeplearning.ai
- Neural Networks and Deep Learning
- Improving Deep Neural Networks
- Structuring Machine Learning Projects
- Convolutional Neural Networks
TensorFlow in Practice Specialization, deeplearning.ai
- Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
- Convolutional Neural Networks in TensorFlow
- Natural Language Processing in TensorFlow
AWS Machine Learning Blog
- Accelerate and improve recommender system training and predictions using Amazon SageMaker Feature Store
- Extend model lineage to include ML features using Amazon SageMaker Feature Store
- Bring your own model with Amazon SageMaker script mode
Conference talks & media appearances
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.
- 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.