machine learning

Explainability in AI is important for gaining trust in model predictions and is extremely essential for improving model robustness. Good explainability often acts as a debugging tool, revealing flaws within the model training process....

, I worked on real-time fraud detection systems and suggestion models for product corporations that looked excellent during development. Offline metrics were strong. AUC curves were stable across validation windows. Feature importance plots told...

is a to Optimizing Data Transfer in AI/ML Workloads where we demonstrated using NVIDIA Nsight™ Systems (nsys) in studying and solving the common data-loading bottleneck — occurrences where the GPU idles while it waits for input...

I actually have been working within the Analytics space for over 20 years. Back then, it was not called “analytics”, it was “Business Intelligence” and even “Decision Support Systems” in older times. The terms...

I the concept of federated learning (FL) through a comic by Google in 2019. It was a superb piece and did a fantastic job at explaining how products can improve without sending user...

that linear models will be… well, stiff. Have you ever ever checked out a scatter plot and realized a straight line just isn’t going to chop it? We’ve all been there. Real-world data is...

1. Introduction two years, we witnessed a race for sequence length in AI language models. We regularly evolved from 4k context length to 32k, then 128k, to the huge 1-million token window first promised...

Helps in Time Series Forecasting All of us understand how it goes: Time-series data is hard. Traditional forecasting models are unprepared for incidents like sudden market crashes, black swan events, or rare weather patterns....