GenAI

The Gen-OS Newsletter - Is DareData Changing?

DareData's future is here!

Business, Management

How to Retain Talent

How DareData is retaining top talent

Tackling Technical Debt is Crucial for Long-Term Success

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

Artificial Intelligence, AI Leadership, Project Management

Tips on How to Manage Large Scale Data Science Projects

use these tips for project management with data science and machine learning

Agents, Artificial Intelligence, Business

AI Agents for Customer Support

Learn how AI Agents can transform your customer support processes

AI Leadership, Management, data strategy

Organizations’ Machine Learning Investment is (or should be) Incremental

Embedding ML systems into production is still a hard thing to do (for most companies)

Fundamentals of Geospatial Data Analysis

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

Artificial Intelligence, PyTorch

PyTorch Introduction — Training a Computer Vision Algorithm

Learn how to train a computer vision algorithm using Pytorch

Artificial Intelligence, AI Leadership, Business, GenAI

You need an AI Team and an AI Lead

Having an AI Team and AI Lead today is comparable to the need of having an IT Team 20 years ago.

GenAI, LLM, Agents, Artificial Intelligence

3 Use Cases for Generative AI Agents

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

Artificial Intelligence, PyTorch, Data Science

PyTorch Introduction — Using Custom Data

In this post of the PyTorch Introduction, we’ll learn how to use custom datasets with PyTorch, particularly tabular, vision and text data

Artificial Intelligence, Data Science, Technical, PyTorch

PyTorch Introduction — Enter NonLinear Functions

Continuing the Pytorch series, in this post we’ll learn about how non-linearities help solve complex problems in the context of neural networks

Artificial Intelligence, Technical, PyTorch

PyTorch Introduction — Building your First Linear Model

Learn how to build your first PyTorch model, by using the “magical” Linear layer

Artificial Intelligence, Data Science, PyTorch, Technical

PyTorch Introduction - The Tensor Object

Learn about Tensors and how to use them in one of the most famous machine learning libraries, PyTorch

Business, Best data practices, MLOps

Why teach MLOps to your Data Science Teams?

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

Data Engineering, Data Ops, Data Pipelines

Tips to Build a Robust Data Lake Infrastructure

Learn how we build data lake infrastructures and help organizations all around the world achieving their data goals.

Artificial Intelligence

Why this time it may be different - Digesting Large Language Models and their implications for Society

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.

The Lean Structure Startup

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

Business

2023, prepare to take-off

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

Data Engineering

Most important Data Engineering Concepts and Tools for Data Scientists

Learn the most important data engineering concepts that data scientists should be aware of - such as data orchestrators, ingestion or infrastructure-as-code

Business, Data Science

DS Building Blocks - Regression vs. Classification

Understand the difference between regression and classification problems

Business, Management, Best data practices

Good data teams / Bad data teams

Understand the main characteristics that set good and bad teams apart.

Technical, Geodata, Geospatial

Locate your Data and Boost it with Geo-Processing

In this post, we'll check how we can work with geographical data using Python.

Business

The best AI safeguards: thoughtful human beings

To make sure your ML strategy doesn't wreak havoc on your brand, data and business teams should be more concerned with the "why" rather than the "how".

Business, Technical

Why hire brilliant when average will do?

About meI've recruited, hired, and worked directly with ~50 technical workers over the last 8 years. And by "worked with" I mean actually worked with. Gone to client meetings with