When your model learns more than it should There is a quiet assumption in most machine learning projects. If the model is accurate, then it must be good. That assumption works well, until the model starts making decisions about people, money, risk, or opportunity. Then accuracy alone is not enough. A model can be very accurate and still systematically treat groups differently. This is the ninth article in our series "The Theory behind Qlik Predict". So far, we have talked a

Machine Learning has become more accessible than ever. With tools like Qlik Predict and other AutoML platforms, building a model no longer requires writing code or tuning algorithms manually. But accessibility does not mean simplicity. In this video, Igor Alcantara walks through the Top 10 Mistakes in a Machine Learning project, especially in AutoML environments. These are the errors that quietly destroy performance long before an algorithm fails. From vague business questio

The landscape of data is shifting beneath our feet. For years, the gold standard of business intelligence was the dashboard, a reliable way to visualize what happened in the past. But today, that is no longer enough. The modern professional must engineer systems that predict what will happen and provide answers as to why. AI is no longer a future concept; it is a reality that is here to stay, and those who do not master these tools risk being left behind. Are You AI-Ready? At

ABSTRACT Retrieval Augmented Generation (RAG) has become a dominant approach for question answering over large collections of unstructured documents. The UDA (Unstructured Document Analysis) benchmark provides a rigorous suite for evaluating RAG systems on real world long documents. This paper presents a large scale, faithfulness oriented evaluation of Qlik Answers on a UDA subset composed of unstructured PDF reports. A corpus of 136 documents (19,959 pages) and 11,842 questi

This video is another one on the series of MCP capabilities, the protocol that allows you to use LLM to control and integrate one or more APIs through a conversational flow. In this video, we show how we can integrate any data source to Qlik Predict, using it as the source of a prediction and also as the output of a give deployment. Watch the previous videos on this series: 01: https://www.youtube.com/watch?v=-mOx8OcU3sU 02: https://youtu.be/4y10UVuzX64 03: https://youtu.b

Author: Igor Alcantara Every dataset has a bit of Dr. Jekyll and Mr. Hyde in it. On the surface, it might look like one thing, but underneath it can take on multiple identities depending on how you examine it. In fact, sometimes it’s not just two identities; it’s ten. That’s where classification comes in. Unlike regression, my previous article, which predicts how much or how many, classification sorts data into categories, revealing its hidden personas. Sometimes the split i

Q(p) = Regression - Headache Author: Igor Alcantara When someone says “regression,” your brain might first jump to childhood flashbacks of questionable fashion choices or to the time you fell back into eating instant noodles for dinner three nights in a row. Or maybe regression is the haircut I used in the 1990's when I was the singer and lead guitar player in a rock band (see below). But in the world of data science, regression is about moving forward using past data to pred

Real photo of Igor Alcantara in a regular ML project Author: Igor Alcantara There is a common trend in the world of data that is a bit dangerous. A person watches a couple of YouTube videos, learns how to use some no-code tools, Auto ML type or RAG, and claims "I am a data scientist" or learn a few prompts and say they are an "AI Expert". What bothers me is not the title itself. As I explore in a previous article , titles only matter if you do not see beyond the words. What c

Author: Igor Alcantara Imagine your data as a cluster of clocks orbiting a massive gravity mass: sales flowing by the minute, inventory pulsing in discrete beats, marketing spend stretching and contracting like spacetime around the edge of a black-hole. On their own, each clock tells a fragment of the story; together, their shifting rhythms bend forecasts the way relativity bends light, revealing hidden paths through uncertainty. Multivariate time-series forecasting captures

Author: Igor Alcantara Let's board our Delorean and travel back in time a little. Not to 1955 or even 1985 but just a few months: April 2024. Back then, I started a series of articles aimed to explain the theory behind Qlik Predict . I started explaining the explanation of Machine Learning predictions. In other words, my first article in this endeavor was about SHAP Statistics . A few months later, I wrote about Qlik Predict Preprocessing tasks, and my very last article of 2

The image says Qlik AutoML because this was the name of Qlik Predict when this article was written Author: Igor Alcantara Welcome back, fellow data adventurers! If you've been journeying with us, you've already navigated the treacherous terrains of preprocessing tasks in Qlik Predict , and unlocked the enigmatic secrets of SHAP values and feature importance . There articles are part of my series where I explain the theory behind Qlik Predict . I hope you are a ready for the t

Author: Igor Alcantara We take a lot of things for granted. For those new to Machine Learning who are lucky to start with tools like Qlik Predict , there are a lot of things happening behind the scenes that veterans (aka classical practitioners) like me had to program in R and Python in the past, just like the old Aztecs did (well, not really, but I like the joke). Don't get me wrong - this is amazing. We can now focus more on the analytical and scientific parts of the work r

Hello, Data Explorers! 🚀 In this video, we show you how access a deployed AutoML model straight from a Python code. You will learned about AutoML deployment, Real-Time API, API Keys, and how to implement this solution in Python. We will also mention a couple of use case scenarios for a solution like this. 👍 If you appreciate this video, like it and share it. 🔍 Follow us: Youtube: https://youtube.com/@datavoyagers Linkedin: https://www.linkedin.com/company/datavoyagers

Hello, Data Explorers! 🚀 Today, we're doing something different. We're doing a react video! Igor Alcantara is reacting to the next features of Qlik AutoML, which will become available around mid July 2024. 👍 If you appreciate this video, like it and share it. 🔍 Follow us: Our blog: https://www.datavoyagers.net/ YouTube: Data Voyagers Channel ( youtube.com )

Join Us for an Exclusive Webinar on Qlik AutoML We are excited to invite you to our upcoming webinar, "Qlik AutoML in Action: Practical Steps from Data Preparation to Real Insights" This event is designed to provide you with a deep dive into the world of Qlik AutoML, from the essentials of data preparation to the advanced techniques of predictive and prescriptive analytics. Date : Jul-16-2024 Time : 04:00pm EST Duration : 2 hours During this session, you'll learn: - The fun

Author: Igor Alcantara Have you ever wondered how economists predict market trends, or how biologists understand the relationship between environmental factors and species behavior? This is where regression analysis comes into play, a cornerstone in the world of statistics with applications spanning finance, economics, biology, and social sciences. It's a method that dives deep into the dynamics between variables, offering insights into how changes in one or more independent

Author: Igor Alcantara In today's data-centric landscape, much fanfare surrounds the predictive prowess of machine learning, often casting it as the beacon of the digital age. Yet, while prediction is undeniably a potent facet, the true crown jewel of machine learning lies in its prescriptive capabilities. Moving beyond simply forecasting future events, the power of prescriptive analysis provides actionable insights and recommendations, enabling organizations to not just fore