In this article, I present my latest open-source project — Government Funding Graph.
The inspiration for this project came from a desire to make better tooling for grant writing, namely to suggest research topics, funding bodies, research institutions, and researchers. I have made Innovate UK grant applications in the past, so I have had an interest in…
Deploying your Large Language Model (LLM) is not necessarily the final step in productionizing your Generative AI application. An often forgotten, yet crucial part of the MLOPs lifecycle is properly load testing your LLM and ensuring it is ready to withstand your expected production traffic. Load testing at a high level is the practice of…
This is the first in a multi-part series on creating web applications with Generative Ai integration.
Table of Contents
Introduction
The Virtues of the Application Layer
Thick Wrappers
The Return of Clippy
Getting Stuff Done While You Sleep
Introduction
The AI space is a vast and complicated landscape. Matt Turck famously does his Machine Learning, AI,…
Working with products, we might face a need to introduce some “rules”. Let me explain what I mean by “rules” in practical examples:
Imagine that we’re seeing a massive wave of fraud in our product, and we want to restrict onboarding for a particular segment of customers to lower this risk. For example, we found…
I’m definitely not the only person who feels that YouTube sponsor segments have become longer and more frequent recently. Sometimes, I watch videos that seem to be trying to sell me something every couple of seconds.
On one hand, it’s great that both small and medium-sized YouTubers are able to make a living from their…
In my previous article, I discussed how morphological feature extractors mimic the way biological experts visually assess images.
This time, I want to go a step further and explore a new question: Can different architectures complement each other to build an AI that “sees” like an expert?
Introduction: Rethinking Model Architecture Design
While building a…
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Least Squares is used almost everywhere when it comes to numerical optimization and regression tasks in machine learning. It aims at minimizing the Mean Squared Error (MSE) of a given model.
Both L1 (sum of absolute values) and L2 (sum of squares) norms offer an intuitive way to sum signed errors while preventing…
As we have already seen with the basic components (Part 1, Part 2), the Hadoop ecosystem is constantly evolving and being optimized for new applications. As a result, various tools and technologies have developed over time that make Hadoop more powerful and even more widely applicable. As a result, it goes beyond the pure HDFS…
Which Outcome Matters? Here is a common scenario : An A/B test was conducted, where a random sample of units (e.g. customers) were selected for a campaign and they received Treatment A. Another sample was selected to receive Treatment B. “A” could be a communication or offer and “B” could be no communication or no…
In the world of machine learning, we obsess over model architectures, training pipelines, and hyper-parameter tuning, yet often overlook a fundamental aspect: how our features live and breathe throughout their lifecycle. From in-memory calculations that vanish after each prediction to the challenge of reproducing exact feature values months later, the way we handle features can…