How to Become a AI Product Visualization Designer

Build an interactive dashboard using Streamlit that takes a pre-trained image classifier (e.g., from TensorFlow Hub) and allows users to upload images. The dashboard should display the prediction, confidence score, and a visualization of the model's attention (e.g., using Grad-CAM) to explain why it made that prediction.

Define a brand's visual language (colors, shapes, typography, mood) and use Midjourney/DALL-E to generate a consistent set of icons, hero images, and UI components that adhere to this guide. Document the prompts and parameters used for reproducibility.

Create a web app (e.g., with Dash or a custom JS frontend) that visualizes a regression model's predictions. Users should be able to adjust input feature sliders and see the predicted output update in real-time, with the ability to compare scenarios side-by-side.

Design and build a monitoring dashboard (using Grafana or custom) that visualizes the health of an ML system. It should show data drift metrics, model performance (accuracy, latency) over time, and alert when thresholds are breached. Use a simulated data pipeline.

Create a long-form, scrollytelling web page (using Scrollama.js or similar) that explains the transformer architecture and text generation process. Use animations, interactive diagrams, and small code examples to make concepts like tokenization, attention, and sampling intuitive.