Building AI Agents with LLMs and Custom ML Models: The Blueprint for Enterprise-Ready Autonomy

Introduction: The Rise of Hybrid AI Agents AI agents are rapidly emerging as the next abstraction layer for enterprise artificial intelligence. Rather than interacting with models directly, organizations are beginning to deploy systems that can perceive inputs, reason over context, and take action with limited human intervention. Large language models have accelerated this shift dramatically,... Continue Reading →

January 21, 2026 0

The Transformer Architecture: Foundations, Engineering Trade-Offs, and Real-World Deployment at Scale

I know that many resources explain this architecture, including the pivotal paper Attention Is All You Need. I wanted to write this to cement the concepts in my mind. This architecture is what is driving the current AI revolution, so it is essential to have a good grasp of the ideas. Since its introduction in... Continue Reading →

January 16, 2026 0

The Classification Task: From Data to Decisions

In the first article, I focused on how a data science repository is structured and why that structure supports MLOps practices like repeatability, traceability, and safe iteration. In this follow-on post, I want to zoom in on the machine learning problem itself: the classification task implemented in the repository. The goal here is not to... Continue Reading →

January 12, 2026 0

When Machine Learning Repositories Are Designed Like Software Systems

In a continuation of capturing lessons learned while getting my Master’s in Data Science from Boston University, I wanted to focus on how to create a real world project that is repeatable. Most machine learning projects don’t fail because the model is bad. They fail because the project can’t be reproduced, automated, or safely evolved... Continue Reading →

January 7, 2026 1

Interpreting Machine Learning Results: Beyond the Accuracy Score

Machine learning models have transformed industries, from diagnosing diseases to predicting customer behavior. However, building a robust model is just half the battle. The real value comes from understanding what your ML results mean and how to communicate them responsibly. For data scientists, ML engineers, and tech professionals alike, interpreting machine learning results is as... Continue Reading →

December 22, 2025 0

The Four Types of Machine Learning: A Friendly Guide for Aspiring Data Scientists

Choosing the right path for your data can unlock incredible insights. Let’s demystify the main ways machines learn, so you can pick the best approach for your data science journeys. As I finish up my Master’s in Data Science from Boston University, I want to reflect on what I learn. This is the first post,... Continue Reading →

December 19, 2025 0

Why Data is the Lifeblood of Generative AI

How data quality and quantity affect the performance and potential of generative models Generative AI is a branch of artificial intelligence that focuses on creating new content or data from scratch, such as images, text, music, or speech. Generative models can learn from existing data and generate novel and realistic samples that can be used... Continue Reading →

March 12, 2024 0

MLOps with Azure

Introduction Machine learning (ML) is a rapidly evolving technology that can bring significant value to various domains and applications. However, developing and deploying ML solutions is not a simple task. It requires a systematic and disciplined approach to manage the entire lifecycle of ML projects, from data collection and preparation to model development and evaluation... Continue Reading →

February 19, 2024 1