Post by IntelJungle

In R&D engineering, data analytics is no longer just about plots and reports. AI changes how engineers work with data. Models scan millions of simulation points in seconds, flagging outliers in lab data and grouping test results automatically. AI-based tools can help to compare design variants and rank them by performance, leading to faster design cycles and fewer failed experiments. Engineers can now spend less time searching and more time asking the right questions.   Most teams still use familiar tools like Python and MATLAB, with TensorFlow and PyTorch for models. New AI tools add another layer. Databricks helps combine simulation, test and production data in one workflow. Weights & Biases makes it easy to track experiments, compare runs and see what really changed results. Scale AI helps create clean, reliable datasets for training engineering models. AI can now spot trends, predict failures and suggest next experiments. How is AI changing how you analyze your test data?   #RnD #DataAnalytics #AITools