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  • Robert Murtha

Leveraging User Experience to Enhance Model Training

Updated: Apr 7


A lady who is leveraging psychology to train ML Models

Machines are simple, the models that train them are complex, and the humans that benefit from the value these trained machines provide are complicated. That's deep, right? There are ways that you can leverage User Persona Development, UX Research, and UX Testing to train higher-quality ML Models. This is a trend that you'll see pop up at smart hyperscaling companies, and it will take the rest of the industry some time to catch up. You could be one of the early adopters!


Product design is not just about creating aesthetically pleasing products; it's also about understanding and empathizing with the customers who will use them. This understanding of the customer is essential when creating a machine-learning model that will provide value to them. In this article, we will explore how product design skills can be used to personify customers, making it easier to understand their needs and create models that truly meet their expectations.


Blue and Teal 3D Shapes that are Futuristic.

User Personas and AI/ML


Personifying customers is like creating a pretend version of them, but using real information and understanding who they are. These personas includes details like their age, where they live, what they like, and what problems they have. Doing this helps us picture who they really are and understand their needs better.


UX Professionals use personas to create products that will meet the needs of their customers, in a data-driven way. Similarly, when creating a machine learning model, it's important to personify customers to help define the value that the model will provide. This will ensure that the model is designed with the customer in mind, rather than just being a technical solution.


Personifying customers is also essential when creating machine learning models that will have a direct impact on people's lives. For example, a machine learning model that is used to diagnose medical conditions must be created with the patient in mind, to ensure that the model is providing the right information and recommendations.

To create personas for a machine learning model, it's important to gather data about the customers that the model will serve. This data can come from various sources, including customer surveys, focus groups, and online data. This data will help to create a comprehensive picture of the customers, including their demographics, behaviors, and pain points.


Once the data has been gathered, it's time to create the personas. This can be done by synthesizing the data and creating a fictional representation of a customer. The persona should include details such as their name, age, occupation, and habits. It should also include information about their pain points, and how the machine learning model will help to solve them.



Crazy different gradient shapes that are futuristic looking.


By personifying customers, machine learning model creators can ensure that their models provide real value to the people who will use them. This will help to build trust with customers and ensure that the model is successful in meeting their needs.

In conclusion, product design skills are essential when creating machine learning models that will provide value to customers. Personifying customers helps to put a face and a story to the data, making it easier to understand and empathize with their needs.


Incorporating User Interviews Into AI/ML Development


User interviews are like a goldmine for getting the info you need to build an AI that rocks. They give you the inside scoop on what the target customers need and do. Here's the rundown on how to do it:

  1. Know your audience: Figure out who you're building the AI for and pick a group to talk to.

  2. Ask the right questions: Make a list of questions that'll help you learn about their needs, habits, and pain points.

  3. Chat it up: Schedule some time to talk with the target customers and ask open-ended questions to get them talking.

  4. Make sense of the info: After the interviews, look for patterns and common themes in the answers. This info can be used to build the AI.

  5. Get personal: Use the insights from the interviews to create personas, which are like pretend versions of the target customers. This helps you better understand and empathize with them.

  6. Keep improving: As you build the AI, keep getting feedback from the target customers and make changes based on what they need.

By doing user interviews, AI builders can get to know the target audience and make sure the AI meets their needs and is valuable to them.


This understanding is essential when creating models and algorithms that have a direct impact on people's lives, and it will ensure that the model is designed with the customer in mind. By using product design skills to personify customers, machine learning model creators can create models that truly meet their customers' needs and provide real value to them. Happy training!


Adjective is a design company that focuses on user experience, human factors, and AI Development. The company combines deep technology with human-centered principles to ensure solutions are developed in high-quality and humane ways. Automation is good when it considers the human impact. Currently we are collaborating with exceptional partners in a variety of industries such as: Defense, Academia, Medicine, and Energy. We are a NIST 800-171 compliant company.

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