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 →
When Microsoft took the stage at Ignite 2025 conference, it wasn’t just unveiling another enterprise tech upgrade, it was heralding a seismic shift in the landscape of business AI. With the provocative declaration of 2026 as the “Year of the Agent,” Microsoft placed its newly rebranded Microsoft Foundry platform at the heart of this transformative... Continue Reading →
In the world of large language models (LLMs), few topics generate more intrigue—and complexity—than memory. While we've seen astonishing leaps in capabilities from GPT-3 to GPT-4o and beyond, one crucial bottleneck remains: long-term memory. Today’s LLMs are incredibly good at reasoning over the contents of their prompt. But what happens when that prompt disappears? How... Continue Reading →
The financial services industry is at the forefront of a technological revolution. Artificial Intelligence (AI) is more than just a buzzword; it offers unparalleled opportunities for financial institutions to innovate, streamline operations, and improve customer experiences. This blog post will discuss three compelling use cases for AI in financial services organizations. Before you start incorporating... Continue Reading →
In this blog, I wanted to experiment with Copilot. I gave it a prompt to write a story about an adventurer who got their bank account hacked. The main character of the story is a LLM Chatbot. I was curious if Copilot could create a realistic scenario. I think financial institutions could use LLM Chatbots... 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 