One way to ease the burden of organizational efforts is to enable companies to drill down through terabytes of complex AI models and neural networks to specific data sets. These advanced ML models become far more accessible to the end-users by empowering users to create custom visualizations. New research from StreamSets helps organizations discover and manage hidden AI and ML models in time to optimize the performance of models that power next-generation AI systems.

Big data has never been more popular. No matter your industry, the amount of data you collect has grown exponentially. And the availability of data through new technologies, including cloud, analytics, and machine learning tools, continues to expand.

Artificial intelligence (AI) and machine learning (ML) promise to be transformative to a broad range of industries, including customer service, financial services, healthcare, retail, finance, automotive, etc.

Customers are expecting AI-powered experiences to improve productivity and simplify operations. But with new technologies come new challenges, including the management and governance of an unprecedented amount of data, as well as privacy concerns.

The volume and velocity of unstructured data have never been greater, and the volume of unstructured data that needs to be learned has never been greater. It's no longer just an AI and ML problem: Companies now need an advanced data integration platform to find, access, ingest, and analyze all of their data in one place. This need necessitates sophisticated business rules and rules-based strategies.

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Why Would You Want to Visualize Data? 

You might want to communicate complex insights, analyze and compare different data types, or create dashboards for benchmarking. Or, perhaps you simply want to visualize and illustrate your data for analysis or education.

It's a significant step up from plotting figures on your desktop using a mouse. Not only does visualization allow you to present complex data in the form of visuals, but it also frees you from using the mouse, which is a task that no longer takes priority on the desktop. Some of the many benefits you have of visualizing data include:

  • More, detailed, and measurable results
  • Driven by real-time insights
  • Seeing the gaps between resources and goals
  • Knowing what's happening and why

Unfortunately, there are many more things to consider when choosing a desktop tool. Think about ease of use, accessibility, scalability, and collaboration with other users (see the list below).

At work, your machine may not have the capability of handling the large number of graphics or interactive elements you are trying to output. This is where desktop graphic design tools such as Illustrator are essential.

Tools You Can Use to Visualize AI and ML Data

An increasing number of tools coming onto the market allow you to visualize AI and ML data. Below is a list of Open Source solutions you can use to get started:


HiPilot is a basic interactive visualization tool to help AI/ML researchers discover correlations and patterns in high-dimensional data. 


An easy to use data visualization tool with an extensive toolkit


A tool that allows AI/ML data to be visualized with CSS and JavaScript.


Aligned with the PAIR initiative (Google's People + AI Research program), Facets is an open-source visualization tool that aids in understanding and analyzing ML datasets.


TensorWatch offers many tools, including debugging, but what stands out is the ability to visualize data streams. 

The ability to build AI/ML models is moving quickly from the data scientist's domain towards the citizen developer. Creating results from AI no longer requires a Ph.D. The challenge with AI results, specifically for AI monitoring live environments such as networks, is that it is still challenging to apply tools to visualize and understand the streamed AI results. 

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Using Google to Visualize Data

If you're building a marketing campaign, there are a few different tactics to help you visualize the data. The first is to use tools like Google Earth. Place little pop-ups on the side of a building or the city map, and you'll be able to see the location and percentages of your target audience and the size of the audience you're attracting.

Also, you can get creative and place markers on a map, or use Google Sketchup. In the example below, Google put a marker at each intersection in LA, where traditional methods would have placed a Google Map pin. Then they turned that information into a map. Similarly, you can see the impact of establishing a social media share or hashtag.

You can visualize different types of data in this way, including page views, website visits, and organic search. You can also combine other visualizations for increasingly sophisticated results.

What Are Some Other Considerations?

If you need to perform calculations or crunch numbers, then statistical packages such as SPSS or SAS are good choices. However, they are also more complex and take time to run. This complexity is why you should keep tabs on benchmarking and compare different systems. Management: If you have any power over your machine or a decisive decision-maker, you will want to choose a tool that suits your needs. Remember, you only get one shot at picking the best tool for the job.

Finally, if you want to set up a graphics group at your company, you should use a systematic approach and speak to experts. 

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