I recently organized and presented at a company-wide brunch and learn on the state of AI in 2026. I briefly explained how agentic systems work in vastly oversimplified terms such that less-technical teammembers could follow along. [Read More]

Summary [Read More]

I recently presented at a company-wide all-hands on how AI works, how we got here, and a bit on where we’re going. Only an understanding of simple linear algebra was needed to grasp the learning outcomes. I highted three key concepts for how AI works: [Read More]

Summary [Read More]

Summary [Read More]

Video presentation from 2025 AI in Action conference at LAPU. I discussed using language models and other machine learning techniques to analyze student conversations with AI course assistants.

This is a second post detailing 11 more takeaways I had after recently reading Chip Huyen’s Designing Machine Learning Systems. You can find my first post here. [Read More]

I recently read through Chip Huyen’s Designing Machine Learning Systems, first published in 2022. I found it a highly useful overview of a rapidly evolving field. As I expected from her other writings, Chip provided clear explanations while straddling the line nicely between technical depth and summarization. Machine learning engineering... [Read More]

Streaming analytics refers to the processing and analyzing of data continuously, as opposed to regular batches. Streams are triggered by specific events as the result of an action or set of actions. Examples of these triggering events might include financial transactions, thermostat readings, student responses, or website purchases. Streaming analytics... [Read More]

Principal components analysis (PCA) is a common and popular technique for deriving a low-dimensional set of features from a large set of variables. For more information on PCA, please refer to my earlier post on the technique. In this post, I’ll explore using PCA as a dimension reduction technique for... [Read More]