The best tools for visualizing AI data are notebooks and dashboards. Visualizing, exploring, and interacting with data is often most interesting when it’s all the same metric for comparison at the end. That’s why tools that can produce this kind of imagery are ideal.

Having created visuals using a variety of tools, here are four graphics tools that I’ve found useful. Each tool supports various output types — multiple charts with one picture, graphics that wrap around, and straight lines. They are all excellent.

What Is Machine Learning (ML)?

Let’s start by outlining the scope of machine learning.

  • Machine learning aims to make a system that learns — making decisions without being explicitly programmed to do so.
  • ML is a sub-field of computer science — it sits under AI because it’s about making machines capable of learning.
  • ML involves algorithms that can perform machine learning tasks — say, automatically tagging images and then extracting the objects within the pictures.
  • ML algorithms can be applied to any domain — the fields covered by machine learning include vision (processing images to extract objects and labels), language processing (analyzing text to extract facts), decision making (making predictions based on data), and robotics (trying to develop robots capable of learning).

Machine learning (ML) data is used to generate and refine machine learning algorithms, so it’s critical to understand the data. The data can be extracted from any source — data collected from sensors, video recordings, or human actions.

Now, let’s turn to the best tools for visualizing machine learning (ML) data.


Pandas is a Python library to work with a wide range of data sources. It’s ideal for working with data stored in data warehouses, various data sources, or structured and unstructured datasets.

Pandas come with a wide range of functions — like random-forest, bias-variance models, binary classification, and inverse problems — that you can use to work with your data.

Pandas has various functions, including makes labels, random-forest, logistic regression, random-suffix, gradient descent, and linear regression. The Pandas library includes a general-purpose data science tool, also called Pandas.

Available in the Mac App Store and for Windows.

Pandas is open-source, so if you find it a useful tool, contribute back!


Echo is a web service that helps make it easier to gather and analyze unstructured data. It allows companies to collect data about their customers, employees, or anyone else on the Internet to quickly analyze the data.

  • Echo stores data in Amazon S3 (and you can access data stored in other storage systems, like your laptop).
  • Echo offers two data pipelines: DataPipeline and DiscoveryPipeline.
  • DataPipeline is a data pipeline system for visualizing and analyzing unstructured data.
  • DataPipeline lets you map and populate Elasticsearch with data, then filter the data to generate insights.
  • When it comes time to make more sense of your data, you can export your data in various formats to analyze it further.

Available on AWS.


StatsD is a tool that can help you manage servers, but you can also use it to power various visualization tools.

  • StatsD runs in the background, listening for HTTP requests, and sending events to the front-end.
  • When something happens, it sends the events over the network to a series of Graphite servers, where they are logged.
  • Graphite collects events from StatsD and displays them in a variety of ways.
  • If you find that StatsD has become a bit too busy to handle your requests, you can force it to send fewer events.

Available in the Mac App Store and for Windows.


FlowingData is an open-source data visualization tool that makes it easier to understand large data sets — like posts, tweets, and other web content.

  • FlowingData helps you make sense of the complex data in a variety of ways.
  • It comes with a wide range of visualization tools, like pie charts, line graphs, scatters diagrams, heatmaps, and more.
  • FlowingData lets you search the data, find insights, and see what other people have found.

Available in the Mac App Store and for Windows.

Why Would a Company Want to Visualize AI  Data?

Visualizing AI data is a crucial practice for companies engaged in artificial intelligence and machine learning for several reasons:

1. Understanding Complex Data

AI models often deal with vast amounts of data that can be complex and multidimensional. Visualization helps stakeholders to understand the patterns, trends, and anomalies within this data, making it easier to grasp how the AI model interprets and processes information.

2. Model Debugging and Optimization

By visualizing the operations of their AI models, including how data flows through the model and how decisions are made, developers can identify bottlenecks, errors, or areas of inefficiency. This insight is invaluable for debugging and optimizing model performance.

3. Enhancing Decision Making

Visualizations can make it easier for decision-makers to understand AI outputs and their influencing factors. This clarity can lead to more informed decisions about deploying, adjusting, or scaling AI solutions.

4. Communicating Results

Visual representations of AI data and model performance can be more accessible to non-technical stakeholders, including investors, executives, or customers. This can facilitate better communication about AI initiatives' benefits, limitations, and progress.

5. Trust and Transparency

As AI systems become more integral to operations, there's a growing demand for transparency and explainability. Visualizing how AI models make decisions can help build trust among users and stakeholders by providing insights into the model's reasoning.

6. Training and Education

For those new to AI, visualizations serve as an educational tool, helping them understand complex AI concepts and the importance of data quality, model selection, and algorithmic fairness.

7. Monitoring and Maintenance

Once AI models are deployed, continuous monitoring is essential to ensure they perform as expected. Visualization tools can track model performance over time, highlight when models may be drifting from their trained parameters, or signal when retraining is necessary.

Do AI users typically expect the dashboards to be visual or textual? How can dashboards best suit different needs?

Dashboards are usually visual, but some AI practitioners prefer to use textual dashboards. The main reason is that one would like to see what the AI software outputs look like before the system converts them to graphical form. A textual representation can offer a more granular look at the data than a visual representation does. In these cases, you may opt for a textual diagram.

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What Does the Future Hold for Visualizing AI Data?

Over the next decade, the use of AI will increase and provide better and more accurate results. Expect more dashboards that let users compare their predictions to the products and see how well the system could predict the future. Dashboards will also show more accurate results due to the improved accuracy of the AI software and the deep learning methods being used.

If you are interested in formal training in data analytics and visualization, look at Simplilearn’s data analytics courses, such as Introduction to Data Analytics.  Simplilearn’s collaborative program with Purdue University, the Data Analytics Certification Program, can give you a complete foundation for a data analytics career. If your interest lies in advancing your AI and ML career, there is also a Post Graduate Program in AI and Machine Learning, also a collaboration between SImplilearn and Purdue.

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