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...