OUR TEAM
Zach Deane-Mayer
FOUNDER
AI executive & Kaggle Grandmaster: 15+ years building with AI and ML.
Competitions
grandmaster15+
Years in Artificial
Intelligence6
Patents
Filed2
Classes
taught10,000+
Models
trained
Team
Executive with
15 years of experience
building and
leading AI engineering
teams.
2022–2024
Founded and led the Generative AI team at DataRobot, launching two major products in 2023: Vector Databases and LLM Playgrounds, establishing DataRobot as a pioneer in Generative AI.
2019–2024
Founded and led the Visual AI team at DataRobot, automating deep learning for image and tabular data.
2018–2024
Authored innovative technologies resulting in 2 granted patents (US11386075B2, US11334795B2) and 4 published patent applications (US20210287089A1, US20230067026A1, US20230065870A1, and US20230091610A1).
2015–2024
Developed and refined core algorithms for DataRobot's AutoML platform, establishing a repository of over 500,000 modeling pipelines and meta-learning heuristics.
2015–2024
Grew DataRobot's Machine Learning team from 5 to 70 employees
2020–2022
Led the integration of foundational models into core DataRobot, significantly enhancing image and text modeling performance.
2019
Personally recruited, interviewed, and hired 10 senior machine learning engineers in 2019.
2013
Built a graph-based recommendation engine for Cognius at Cogo Labs, serving 100 million daily recommendations to 2.5 million unique users, resulting in a 70% company-wide revenue increase.
MY experience
Tech Stack
Hugging Face
🤗 Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.
PyTorch
PyTorch is a Python package offering tensor computation with GPU acceleration, deep neural networks via a tape-based autograd system.
Keras
Keras 3 supports JAX, TensorFlow, and PyTorch for easy model building in computer vision, NLP, audio, timeseries, recommender systems.
NumPy
NumPy is the fundamental package for scientific computing with Python.
scikit-learn
Scikit-learn is a Python module for machine learning built on top of SciPy.
XGBoost
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable.
classes I teach
Advanced Deep Learning with Keras
This course shows you how to solve a variety of problems using the versatile Keras functional API. You will start with simple, multi-layer dense networks (also known as multi-layer perceptrons), and continue on to more complicated architectures. The course will cover how to build models with multiple inputs and a single output, as well as how to share weights between layers in a model. We will also cover advanced topics such as category embeddings and multiple-output networks. If you've ever wanted to train a network that does both classification and regression, then this course is for you!
Machine Learning with caret in R
Machine learning is the study and application of algorithms that learn from and make predictions on data. From search results to self-driving cars, it has manifested itself in all areas of our lives and is one of the most exciting and fast growing fields of research in the world of data science. This course teaches the big ideas in machine learning: how to build and evaluate predictive models, how to tune them for optimal performance, how to preprocess data for better results, and much more. The popular caret R package, which provides a consistent interface to all of R's most powerful machine learning facilities, is used throughout the course.