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AI/ML Product Design Guide•3 min read

Simplified ML Process for Product Designers (Diagram)

Simplified ML Process for Designers.

Simplified ML Process for Product Designers (Diagram)
• This post displays a simplified machine learning (ML) process.
• The ML process for a small dataset running on a personal computer.
• Where a product designer might fit into the ML process.

Learning the language is one of the most difficult things about AI/ML technology. It sounds foreign to designers and sounds very technical. But I've broken down the basic ML process for you so you can get comfortable with the overall process and the steps involved.

Take a minute and scan the process. I've pointed out where designers might fit into the process to give you a perspective on how you might work with a data team. I encourage you to learn the technical aspects of the ML process so that you can work more closely with AI/ML engineers and data scientists.

ML Process Diagram

Simplified machine learning process explained for product designers - The Triangle Offense

Process Diagram Explained

  1. ML is valuable to a business because it can help sort large datasets and look for patterns that can help solve business problems. As a designer, you would also need to understand those business problems.
  2. Next, you must compile the right data into a CSV file. You would pull this data from various databases across your company.
  3. Then, you will need to create a development environment to upload the CSV data file and work with it.
  4. After uploading the CSV data file to your Jupyter Notebook, you must select an algorithm to process the data.
  5. Take your data and split it into training data and test data.
  6. You first process the training data with the chosen algorithm. Here, you tweak your algorithm and iterate.
  7. After working through your hypothesis on your training data, you select the algorithm to run on the test data and look for errors.
  8. Now, you have a working data model that can predict and solve a business problem.
  9. It is time to evaluate the working data model, tweak it if necessary, draw insights, and run the outcome by the decision-makers in the product organization.
  10. It's time to wrap a user experience around the prediction/decision.

Designer Perspective

As a product designer, you probably realize that the future of design will be more technical than what you have been used to. Sometimes, we hide in our qualitative, artistic, pixelated world and do not bother with tech's technical or data-driven aspects.

That is all about to change. ML is only one subfield of AI, and there are many other subfields like NLP, knowledge representation, planning, and robotics. The theme is the same. You must become more technically proficient and adapt your thinking from working in a traditional product development team. To work in an AI product development team.

I hope you study the process diagram and learn the steps and language associated with the ML process. If you can learn the language, I am confident that in no time, you will be able to understand what the ML engineers and data scientists on your future AI product team are working on.

Summary

Learning the basic ML process will help you understand where you fit in on a future AI-product team. Building AI-powered user experiences requires new technical knowledge.

References

Written by Leo Vroegindewey, B2B CX Consultant

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