ARTICLES

In this article, we’ll talk about different roles in a data team and discuss their responsibilities. In particular, we will cover: The types of roles in a data team; The responsibilities of each role; The skills and knowledge each role needs to have. Want to listen to...

Avoid these common ML monitoring mistakes – your model’s success hangs in the balance.  So you’ve built a machine learning model that works well in the lab. You’ve validated it, gotten the green light from the internal stakeholders, ensured that it met any regulatory...

When an organization commits to DevSecOps, a fundamental shift takes place across teams. Security becomes everyone’s responsibility. From the beginning of the development cycle, code is reviewed, audited, and tested for security issues. Those issues can be resolved...

Inaccurate models can be costly for businesses. Whether a model is responsible for predicting fraud, approving loans, or targeting ads, small changes in model accuracy can result in big impacts to your bottom line. Over time, even highly accurate models are prone to...

Introduction Common reasons behind computer vision projects failing are (1) a failure to make it to production, (2) the time where your coveted computer vision scientists and engineers spend too much of their time on menial tasks, and (3) increased governance risk. It...

Your model is only valuable when it's used In the past 6 months, I have spoken to representatives of nearly a hundred companies and had insightful conversations about the data science activities in their organisations. Besides discovering and rediscovering many times...

When you think of machine learning’s biggest breakthroughs in the last decade what comes to mind? AlexNet dominating ImageNet in 2012 and unleashing the era of deep learning? Self-driving cars navigating the complex and chaotic streets of a big city? Massive...

Let’s transform fairness from an abstract goal into a reality for machine learning models. Machine learning is used ubiquitously in applications like facial recognition and online advertisements — however, many of these ML models show clear evidence of unintentional...

Why feature engineering and feature management is as important to your ML project as the algorithm you choose There are endless articles and tutorials on topics in Data Science and ML, from Scikit-learn to Keras to PyTorch. In just a few hours, you can create a GAN to...

Supervised Machine Learning projects typically require labeled data to train the algorithms. You want to use high-quality data that corresponds to the problem you are trying to solve. But how do you obtain this kind of data? In this session, Magdalena Konkiewicz shows...