Many NLP systems show strong results in controlled environments but struggle when deployed.

Is this mainly due to data drift, lack of context understanding, or limitations in how models generalize beyond training data?

Interested in how others are addressing this gap between performance and real-world reliability.

When building dashboards or reports, selecting the right chart type can make a big difference in how clearly insights are communicated. For example, when should you use a bar chart, line chart, scatter plot, or heatmap?

What principles or best practices do you follow when choosing visualizations for analysis or dashboards? Do you prioritize simplicity, storytelling, or comparison when deciding which chart to use?

Would be great to hear how others approach this in real projects.