How can you use data dashboards to monitor your ML model's performance?

1 Why use data dashboards?

Data dashboards are interactive displays that can help you understand the results of your ML models in a simple and straightforward manner. These dashboards can answer questions like how accurate your model is on new data, how it compares to other models or benchmarks, how it performs on different segments of data, and how it affects your business goals. Furthermore, data dashboards can be used to communicate the performance of your model to various audiences, such as managers, clients, or users. They can help explain how the model works, what value it provides, and what limitations or challenges it faces.

2 How to design data dashboards?

Data dashboards should be designed with specific goals and questions in mind. You should select metrics and visualizations that best reflect your model's performance and relevance. Common metrics and visualizations for ML models include accuracy, precision, recall, F1-score, mean absolute error, root mean squared error, R-squared, confusion matrix, ROC curve, precision-recall curve, feature importance, partial dependence plots, SHAP values, histograms, box plots, scatter plots, bar charts, pie charts and heat maps. Additionally, you should consider the layout and interactivity of your data dashboard. Use a logical and consistent structure such as tabs, filters and menus to organize your dashboard. Enable features like tooltips, zooming and drill-downs to allow users to explore your dashboard in more detail.

3 How to build data dashboards?

Data dashboards for ML models can be created with a variety of tools and platforms. Tableau is a popular business intelligence and analytics platform that allows users to connect to data sources, create interactive dashboards, and publish them online or on mobile devices. Power BI is a cloud-based analytics service that integrates with Microsoft products, enabling users to import, transform, and analyze data from various sources. Dash is a Python framework that enables users to build web-based dashboards with HTML, CSS, and JavaScript. It also allows users to leverage the power of Python's ML libraries, such as scikit-learn, TensorFlow, and PyTorch. These dashboards can be deployed and hosted on a user's own server or on a cloud platform.

4 How to evaluate data dashboards?

Data dashboards are not static products, but dynamic processes that require constant evaluation and improvement. To optimize them, you should monitor and update your dashboards regularly, using feedback and data. You can test your dashboards with real users and data, collecting their opinions and suggestions through surveys, interviews, or usability tests. Additionally, it's important to review your dashboards with stakeholders and clients, making sure that they meet their expectations and needs by presenting them with reports or demos. Lastly, you can analyze your dashboards with data and metrics to identify their strengths and weaknesses. Tools such as Google Analytics, Mixpanel, or Hotjar can help track user interactions with your dashboards, like clicks, views, and sessions.

5 How to learn data dashboards?

Data dashboards are an invaluable tool for ML practitioners and enthusiasts. Not only do they improve projects and career prospects, but they also demonstrate data literacy and creativity. If you’re looking to learn more about data dashboards, there are plenty of resources available. Online courses like Data Visualization and Dashboards with Excel and Cognos, Data Visualization with Tableau, and Interactive Python Dashboards with Plotly and Dash can teach you the basics. Books like Storytelling with Data: A Data Visualization Guide for Business Professionals, The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios, and Data Visualization: A Practical Introduction can guide you through theory and practice. Additionally, blogs such as The Data School, The Information Lab, and Storytelling with Data provide tips and examples of data dashboards.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?