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 →
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 →
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 →
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 →
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 →
Dave's Two Cents 