One of the fundamental challenges in any growing company is maintaining effective communication and ensuring that employees remain engaged with the organization's mission. Human cooperation has always been dependent on shared beliefs and narratives. However, as groups grow beyond a certain size, these shared beliefs become harder to sustain. This| DareData Blog
You’ve seen it before: a company starts deploying AI but struggles to move beyond the most high-level use cases. Yet, current AI technology has the potential to power near-fully automated business workflows—so what’s missing? The challenge lies in how you manage the error. In critical business processes,| DareData Blog
DareData will close 2024 with a 5% revenue growth compared to 2023. At first glance, given the rapid growth in our market, one might be tempted to classify this year as underwhelming. However, 2024 has been a transformative year for us. We started the year as a 100% consulting business.| DareData Blog
In software development and other information technology fields, technical debt (also known as design debt or code debt) is the implied cost of future reworking because a solution prioritizes expedience over long-term design. Analogous with monetary debt, if technical debt is not repaid, it can accumulate "interest", making it harder| DareData Blog
use these tips for project management with data science and machine learning| DareData Blog
Learn how AI Agents can transform your customer support processes| DareData Blog
Embedding ML systems into production is still a hard thing to do (for most companies)| DareData Blog
In Geospatial Data Analysis, the primary objective is to pose the right questions, leveraging geographic principles to gain insightful answers. Analysts will visualise and decipher patterns through maps while trying to answer those questions. When exploring Geospatial questions, it is also essential to consider temporal aspects. Changes in spatial information| DareData Blog
Learn how to train a computer vision algorithm using Pytorch| DareData Blog
Having an AI Team and AI Lead today is comparable to the need of having an IT Team 20 years ago.| DareData Blog
Discover some examples of Generative AI Use Cases and what how you can level up your organization and business In the dynamic landscape of artificial intelligence, Generative AI agents have taken the center stage when it comes to adding value to organizations' processes. At DareData Engineering, we believe in a| DareData Blog
In this post of the PyTorch Introduction, we’ll learn how to use custom datasets with PyTorch, particularly tabular, vision and text data| DareData Blog
Continuing the Pytorch series, in this post we’ll learn about how non-linearities help solve complex problems in the context of neural networks| DareData Blog
Learn how to build your first PyTorch model, by using the “magical” Linear layer| DareData Blog
Learn about Tensors and how to use them in one of the most famous machine learning libraries, PyTorch| DareData Blog
In today's data-driven world, machine learning has emerged as a transformative force, empowering organizations to extract valuable insights from vast amounts of data. As the scope of the models and the data continues to scale, the role of a Data Scientist has evolved accordingly in the last years. Nowadays, the| DareData Blog
Learn how we build data lake infrastructures and help organizations all around the world achieving their data goals.| DareData Blog
Irrespective of your stance regarding the speed of technological evolution, one thing is certain: with the latest advances in artificial intelligence and the creation of groundbreaking technologies like GPT-4, there will be profound implications for all of us.| DareData Blog
DareData was founded in 2019 by Sam and Nuno with the mission of building a network of exceptional professionals in the fields of Data Science and Data Engineering, all well-paid and personally fulfilled. Today, the company is comprised of four working partners (Nuno, Sam, Ivo and I) and one working| DareData Blog
2022 [done]Hey guys, happy new year! This blog aims to disclose initiatives, achievements and adventures we had during the last year but also to share our difficulties. That is why we would very much appreciate questions, concerns or suggestions you might have around how to grow a data tech| DareData Blog
Understand the difference between regression and classification problems| DareData Blog
Understand the main characteristics that set good and bad teams apart.| DareData Blog
In this post, we'll check how we can work with geographical data using Python.| DareData Blog
To make sure your ML strategy doesn't wreak havoc on your brand, data teams should be more concerned with the "why" rather than the "how".| DareData Blog
